CN104598898B - A kind of Aerial Images system for rapidly identifying and its method for quickly identifying based on multitask topology learning - Google Patents
A kind of Aerial Images system for rapidly identifying and its method for quickly identifying based on multitask topology learning Download PDFInfo
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Abstract
The invention discloses a kind of Aerial Images system for rapidly identifying and its method for quickly identifying based on multitask topology learning, its feature includes:Feature construction module, topological characteristic structure module, topological characteristic projection module, multitask topology learning module, feature selection module and Classification and Identification module;Feature construction module is used to obtain Aerial Images and trains the topological characteristic matrix of Aerial Images;Topological characteristic structure module is used for the topological characteristic projection matrix for obtaining training Aerial Images;Multitask topology learning module is used for the topological dictionary for obtaining Aerial Images;Feature selection module is used for the tuple of feature selecting two for obtaining training Aerial Images;Classification and Identification module is used to build topological Boosting learning models and realizes the classification of target Aerial Images.The present invention can quickly identify the image classification of Aerial Images using multi-task learning framework, improve nicety of grading and efficiency.
Description
Technical field
The invention belongs to image procossing, computer vision and mode identification technology, relates generally to a kind of based on more
The Aerial Images system for rapidly identifying and its method for quickly identifying of business topology learning.
Background technology
Important component of the Aerial Images as remote sensing images, with its substantial amounts of data message and geometric resolution by
Universal attention, therefore it is great that category significance is identified to Aerial Images.The quick identification classification of Aerial Images is a kind of intelligence
Energy image analysis method, its purpose are exactly to carry out fast and accurately identifying and classifying to Aerial Images, are computer vision
One of field hot research topic.The classification of Aerial Images can apply to scene mark, video monitoring, robot path rule
Draw, abnormality detection etc..
With the development of Morden Image Processing Technology, Aerial Images sorting technique has also obtained huge development, but still
So face some following problem:
First, in existing method, zone bit distribution map representativeness is not high in the Aerial Images of structure, it is impossible to accurately represents boat
Clap the constituent of image.
Such as 2011, the author such as Duchenne is in top international conference IEEE International Conference
Delivered on Computer Vision (ICCV)《AGraph-Matching Kernel for Object
Categorization》A kind of objective classification method of the graph structure based on core matching proposed in this article, in this method,
The node on behalf of figure image lattice, and what geometry connection side represented is the annexation between grid, is based on such one kind
The classification of target is realized in the matching of the figure of grid, but because the representativeness of the graph structure of the Aerial Images of structure is not high, it is impossible to
The constituent of Aerial Images is accurately represented, reduces the efficiency of Aerial Images classification.
2nd, the topological structure for considering image is lacked in many Aerial Images assorting processes.
Such as 2010, the author such as Yong Jae Lee is in top international conference IEEE Conference on
Delivered on Computer Vision and Pattern Recognition (CVPR)《Object-Graphs for
Context-Aware Category Discovery》The image classification method proposed in this article, the section of figure in this method
Point represents known and unknown target, and the semanteme of each unknown object utilizes the semantic deduction of its k-space arest neighbors known target
Out, but only only account for the knowledge of spatially adjacent part, i.e., it is semantic to infer another important clue.And topological structure exists
Do not consider during classification, so as to reduce the efficiency of image classification.
3rd, existing many Aerial Images identifications for considering topological relation are classified based on of random walk and tree strategy
Match somebody with somebody, limit the matching range of topological relation in Aerial Images.
Such as 2007, the author such as Harchaoui is in international conference IEEE Conference on Computer
Delivered on Vision and Pattern Recognition《Image Classification with Segmentation
Graph Kernels》A kind of image classification method proposed in this article, this method utilize random walk core and tree kernel function
Method use the space topological of random walk that the adjacent domain of finite sequence goes to extract between topography gray scale and tree to close
System, finally realize that the identification of image is classified using random walk and the matching of tree strategy, but this method has the phenomenon walked haltingly,
So as to introduce noise, the matching of Aerial Images topology is limited, so as to reduce the scope of Aerial Images classification, real life
In hardly result in application.
4th, existing many Aerial Images cannot distinguish between image during classification for the much like image of structure
Space layout, reduce the accuracy of image classification.
Such as 2006, the author such as Svetlana Lazebnik is in top international conference IEEE Conference on
Delivered on Computer Vision and Pattern Recognition (CVPR)《Spatial Pyramid
Matching for Recognizing Natural Scene Categories》The one kind proposed in this article is based on sky
Between the recognition methods of natural scene classification that matches of pyramid, each image is divided into increasingly thinner grid in this method,
Its feature descriptor is calculated by local feature histogram in each grid, but when the space layout very phase of two images
Like when, this method can not be distinguished, and so as to reduce the accuracy of image classification, and complexity is high, it is difficult in practice
It is applied.
5th, the design of the grader of existing Aerial Images classification is substantially based on SVM classifier, and it is more to lack consideration
Information is shared between the grader of individual inter-related task, causes the process that calculates more complicated, the efficiency of classification is low.
Such as 2006, the author such as Svetlana Lazebnik is in top international conference IEEE Conference on
Delivered on Computer Vision and Pattern Recognition (CVPR)《Spatial Pyramid
Matching for Recognizing Natural Scene Categories》This method is set Aerial Images grader
Meter is a kind of non-linear SVM classifier, but due between the multiple inter-related tasks of shortage consideration in assorting process in this method
Information of classifier it is shared, it is not high to result in the accuracy of Aerial Images classification, there is certain limitation.
Therefore, up to the present, still without the nicety of grading and efficiency high for a kind of consideration image topology relation occur
And can be with the Aerial Images sorting technique of engineer applied.
The content of the invention
In place of the present invention is in order to solve the shortcomings of the prior art, a kind of taking photo by plane based on multitask topology learning is proposed
Image system for rapidly identifying and its method for quickly identifying, to which the Fast Classification of Aerial Images can be realized, so as to improve classification
Speed and precision.
The present invention is to solve the problems, such as method scheme with the following method:
A kind of the characteristics of Aerial Images system for rapidly identifying based on multitask topology learning of the present invention, includes:Feature construction
Module, topological characteristic structure module, topological characteristic projection module, multitask topology learning module, feature selection module and classification
Identification module;
The feature construction module obtains some under each image classification respectively according to set N number of image classification
Individual tag along sort, and several training Aerial Images for including itself label under each image classification are obtained respectively;
The feature construction module obtains a target Aerial Images to be identified;By target Aerial Images and described
Aerial Images are trained to form Aerial Images;
Each Aerial Images in the Aerial Images are divided into using simple linear iteration cluster SLIC dividing methods
For multiple zone bits, and form zone bit set;Each area is calculated using probability SVM classifier method to the zone bit set
The comentropy of domain member, and whether the comentropy for weighing each zone bit is less than set information entropy threshold;From the zone bit
The zone bit less than described information entropy threshold is removed in set, so as to generate the zone bit set after optimization;According to the optimization
Positional information of each zone bit in the Aerial Images in zone bit set afterwards, and any two obtained by positional information
The connection side that individual adjacent zone bit is generated, build the area distribution of each Aerial Images in the Aerial Images
Figure;
Several area adjacency subgraphs are generated by the regional distribution chart of each Aerial Images;By area adjacency
The positional information of each zone bit and connection side form the respective topological structure of area adjacency subgraph in figure;By all regions
Adjacent subgraph forms the Region adjacency graph of each Aerial Images;
It is adjacent to several regions of each Aerial Images using Sub-Image Feature construction method and core principle component analysis method
Connect subgraph to be handled, obtain the characteristic vector of several area adjacency subgraphs of each Aerial Images respectively, each region is adjacent
The characteristic vector and the topological structure of respective corresponding area adjacency subgraph for connecing subgraph correspond;By each figure of taking photo by plane
The characteristic vector of several area adjacency subgraphs of picture forms the eigenmatrix of respective Aerial Images in the Aerial Images;It is described
The eigenmatrix of Aerial Images includes the eigenmatrix of target Aerial Images and the eigenmatrix of training Aerial Images;
The topological characteristic structure module will have identical topological structure in the eigenmatrix of the training Aerial Images
Characteristic vector carries out fusion treatment, obtains the topological characteristic matrix of the training Aerial Images;So as to obtain each image
The topological characteristic matrix of all training Aerial Images under classification;
The topological characteristic projection module is according to several described tag along sorts and each training Aerial Images
Itself label and topological characteristic matrix, obtain the topological characteristic projection of the training Aerial Images under each Aerial Images classification
Matrix;
The multitask topology learning module is using multi-task learning framework and principal component analytical method to the topology
Projection Character matrix is iterated, so as to obtain the topological encoder matrix of the training Aerial Images;Using F- normal forms to described
The row vector of topological encoder matrix is calculated, and the result of calculation of acquisition is ranked up in descending order, and S row vector institute is right before selection
The topological structure answered, form the topological dictionary of the Aerial Images;
The feature selection module is according to each topological structure in the topological dictionary and the eigenmatrix of the Aerial Images
In topological structure corresponding to each characteristic vector matched, if the match is successful, by the eigenmatrix of the Aerial Images
The feature selecting vector of the Aerial Images is formed with successful characteristic vector;The feature selecting vector of the Aerial Images includes
The feature selecting vector of the feature selecting vector sum training Aerial Images of target Aerial Images;By the spy of each training Aerial Images
Sign selection is vectorial and the training Aerial Images each corresponding image classification, the feature for forming each training Aerial Images are selected
Select two tuples;
The Classification and Identification module builds multiple disaggregated models to the topological dictionary using SVM classifier;And according to institute
The tuple of feature selecting two for stating training Aerial Images obtains multiple disaggregated models of optimization using probability SVM methods;By described excellent
The multiple disaggregated models changed build topological Boosting learning models;By the feature selecting vector input of the target Aerial Images
Into the topological Boosting learning models, so as to obtain the classification of the target Aerial Images.
A kind of spy of the method for quickly identifying of the Aerial Images system for rapidly identifying based on multitask topology learning of the present invention
Putting is:
It is C={ c to define N number of image classification1,c2,…,cn,…,cN};cnRepresent n-th image classification;Each image point
Several tag along sorts are included in class;Remember n-th of classification cnTag along sort bekn
Represent tag number, 1≤kn≤Kn;Represent kthnIndividual tag along sort;KnRepresent the n-th image classification cnMiddle contingency table
The sum of label;Then the tag along sort collection of N number of image classification C is combined into T={ T1,T2,…,Tn,…,TN};Remember κ for described point
The sum of all tag along sorts in class tag set T, and have
Remember the n-th image classification cnIn comprising the training Aerial Images of itself label beRepresent n-th image classification cnIn mnIndividual training Aerial Images,Represent
The n-th image classification cnIn mnIndividual training Aerial ImagesItself tag set, 1≤mn≤Mn;MnRepresent described
N image classification cnIn it is all training Aerial Images sums;Then the training Aerial Images collection of N number of image classification C is combined into I
={ I1,I2,…,In,…,IN};Remember that target Aerial Images are I0;
The method for quickly identifying is to carry out as follows:
Step 1, structure the n-th image classification cnIn mnIndividual training Aerial ImagesRegional distribution chartSo as to obtain the n-th image classification cnIn it is all training Aerial Images regional distribution chartsTaken photo by plane figure so as to obtain the training of N number of image classification C
Image set closes I area distribution set of graphs RAG={ RAG1,RAG2,…,RAGn,…,RAGN};And the target Aerial Images I0
Regional distribution chart RAG0;
Step 1.1, using simple linear iteration cluster SLIC dividing methods by the n-th image classify cnIn mnIt is individual
Train Aerial ImagesIt is divided intoIndividual zone bit, obtain the mnIndividual training Aerial ImagesZone bit setRepresent the n-th image classification cnIn mnIndividual training boat
Clap image Individual zone bit;The mnIndividual training Aerial ImagesIn include described theIt is individual
Zone bitPositional information;
Step 1.2, utilize the probability SVM classifier method calculating mnIndividual zone bit setInIndividual region
MemberBelong to n-th image classification cnProbabilitySo as to obtain describedIndividual zone bitCategory
The probability each classified in described image classifies C;
Step 1.3, using formula (1) calculate described theIndividual zone bitComentropySo as to obtain
State mnIndividual zone bit setIn each zone bit comentropy:
Step 1.4, judge described information entropyWhether information entropy threshold δ is less than, if being less than, from the mnIt is individual
Zone bit setThe middle zone bit removed less than described information entropy threshold δ;So as to obtain the mnIndividual training Aerial ImagesOptimization after zone bit set
Step 1.5, with the mnIndividual training Aerial ImagesOptimization after zone bit setIn each zone bit
Place-centric point as particle, the particle of the adjacent zone bit of any two in each zone bit is carried out using adjacent side
Connection, so as to obtain the mnIndividual training Aerial ImagesRegional distribution chart
Step 2, structure the n-th image classification cnIn mnIndividual training Aerial ImagesRegion adjacency graph
From the mnIndividual training Aerial ImagesRegional distribution chartMiddle exhaustion includes several of connection side
Particle, the m is formed by several described particles and corresponding connection sidenIndividual training Aerial ImagesArea adjacency subgraphRepresent theIndividual area adjacency subgraph, by the area adjacency subgraph
In each zone bit positional information and connection side form the topological structure of the area adjacency subgraph;Represent the mnIt is individual
Train Aerial ImagesArea adjacency subgraph sum;By the mnIndividual training Aerial Images's
Area adjacency subgraphForm the n-th image classification cnIn mnIndividual training is taken photo by plane
ImageRegion adjacency graphSo as to obtain the n-th image classification cnIn all training Aerial Images region it is adjacent
Map interlinkingSo as to obtain the training Aerial Images set I's of N number of image classification C
Area adjacency set of graphs G={ G1,G2,…,Gn,…,GN};And the target Aerial Images I0Regional distribution chart G0;
Step 3, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixFrom
And obtain the n-th image classification cnTraining Aerial Images eigenmatrix
So as to obtain the training Aerial Images set I of N number of image classification C eigenmatrix set X={ X1,X2,…,Xn,…,
XN};And the target Aerial Images I0Eigenmatrix X0;
Step 3.1, to the mnIndividual Aerial ImagesRegion adjacency graphMiddle particle number identical region is adjacent
Connect subgraphWithSub-Image Feature matrix is respectively obtained using Sub-Image Feature construction methodWith
Step 3.2, utilize formula (2) the acquisition Sub-Image Feature matrixWithGolub-Werman distances
In formula (2),WithThe Sub-Image Feature matrix is represented respectivelyWithIt is orthogonal
Base;||·||2Represent 2- normal forms;
Step 3.3, utilize formula (3) the calculating area adjacency subgraphWithGaussian kernel
In formula (3),Represent Gauss nuclear parameter;
Step 3.4, repeat step 3.1 arrive step 3.3, so as to obtain the mnIndividual Region adjacency graphIn all tools
There is the Gaussian kernel of the area adjacency subgraph of phase homologous pints;
Step 3.5, the m obtained using core principle component analysis methodnIndividual Aerial ImagesEigenmatrixFor describedIndividual area adjacency subgraphFeature to
Amount,D isIndividual characteristic vectorDimension;DescribedIndividual area adjacency subgraphFeature
VectorWith describedIndividual area adjacency subgraphTopological structure correspond;
Step 4, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesTopological characteristic vector
So as to obtain the n-th image classification cnThe topological characteristic matrix of middle training Aerial ImagesSo as to obtain the training Aerial Images set I of N number of image classification C topology
Eigenmatrix set X '={ X '1,X′2,…,X′n,…,X′N};
Step 4.1, retain the n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixIn
In topological structure corresponding to all characteristic vectors topological structure differ and particle number no more than upper limit ν topology knot
Structure, so as to obtain the n-th image classification cnIn mnIndividual training Aerial ImagesTopological structure setSo as to obtain
Obtain the n-th image classification cnTraining Aerial Images topological structure set
Handled using unionSo as to obtain the training Aerial Images set I of N number of image classification C topological structure
Set tp;
Step 4.2, training Aerial Images set I to N number of image classification C topological structure set tp in it is each
Topological structure is numbered, and obtains σ topological structure and is designated as { tp '1,tp′2,…,tp′ο,…,tp′σ};tp′οRepresent that ο are opened up
Flutter structure;1≤ο≤σ;
Step 4.3, by the mnIndividual Aerial ImagesEigenmatrixIn topology corresponding to each characteristic vector
Structure and the ο topological structure tp 'οIt is compared, selects the characteristic vector with identical topological structure and summed simultaneously
Take average processing;Obtain the mnIndividual Aerial ImagesIn the ο topological structure tp 'οOn topology fusion vector So as to obtain the mnIndividual Aerial ImagesIn the σ topological structure { tp '1,tp′2,…,tp
′ο,…,tp′σOn topology fusion vector
Step 4.3, by the mnIndividual Aerial ImagesIn the σ topological structure { tp '1,tp′2,…,tp′ο,…,
tp′σOn topology fusion vectorEnd to end fusion treatment is carried out, is obtained
The mnIndividual training Aerial ImagesTopological characteristic vectorAnd haveD σ=d × σ;
Step 5, the topological projection matrix set M=for training Aerial Images set I for obtaining N number of image classification C
{M1,M2,…,Mn,…,MN}:
Step 5.1, classified c using formula (4) the acquisition n-th imagenMiddle training Aerial Images InTopological transitions matrix
In formula (4),For unit matrix,Represent MnIndividual element is all 1
One dimensional vector,
Step 5.2, classified c using formula (5) the acquisition n-th imagenIn mnIndividual training Aerial ImagesKthn
Individual tag elementSo as to obtain the n-th image classification cnIn mnIndividual training Aerial ImagesLabel vectorSo as to obtain the n-th image classification cnInstruction
Practice Aerial Images InLabel matrixAnd have
Step 5.3, classified c using formula (6) the acquisition n-th imagenMiddle training Aerial Images InLabel measurement instruction
Matrix Fn,
Fn=Yn[(Yn)TYn]-1/2 (6)
Step 5.4, classified c using formula (7) the acquisition n-th imagenMiddle training Aerial Images InTopological projection matrix
Mn,So as to obtain the training Aerial Images set I of N number of image classification C topological projection matrix set M
={ M1,M2,…,Mn,…,MN};
Step 6, multitask topology learning feature coding:
Step 6.1, definition iterations are t;Initialize the iterations t=1;
And the topological encoder matrix of the t times iteration is described in when initializing the t times iterationAnd n-th of topological coded sub-matrices when having the t times iterationIn each member
Element is obtained by random function;
Step 6.2, the topological encoder matrix for obtaining using formula (8) the t times iteration are W(t)Code weight matrix D(t)V row v column elementsCode weight matrix D during so as to obtain the t times iteration(t):
In formula (8),The topological encoder matrix for representing the t times iteration is W(t)V every trades vector, the coding
Weight matrix D(t)For diagonal matrix,
Step 6.3, definition tasking learning number are l;1≤l≤N;And initialize the tasking learning number l=1;
Step 6.4, the weight selection matrix that the t times iteration l subtask learns is obtained using formula (9)In v rows
V column elementsSo as to obtain the weight selection matrix of the t times iteration l subtasks study
In formula (9),Represent l-th of topological coded sub-matrices during the t times iterationV row vectors, the power
Reselection procedure matrixFor diagonal matrix,
Step 6.5, from the topological characteristic projection matrix set M={ M1,M2,…,Mn,…,MNIn extract and open up for l-th
Flutter Projection Character matrix Ml;The code optimization matrix of the t times iteration l subtasks study is obtained using formula (10)
In formula (10), λ presentation code Optimal Parameters;μ presentation code internal coordination parameters;
Step 6.6, note KlFor l-th of image classification c in N number of image classification ClThe sum of middle tag along sort;Using
Principal component analytical method obtains the code optimization matrix of the t times iteration l subtasks studyMultiple characteristic values and with it is multiple
The corresponding characteristic vector of characteristic value, the multiple characteristic value is sorted from small to large, and selects preceding KlCorresponding to individual characteristic value
Characteristic vector as the t times iteration when n-th of topological coded sub-matrices
Step 6.7, judge whether l=N sets up, if so, n times tasking learning is then completed, so as to obtain the t+1 times iteration
Topological encoder matrixAnd perform step 6.8;Otherwise l+1 value is assigned to l, weight
Step 6.4- steps 6.7 are performed again;
Step 6.8, judge whether t=Γ set up, Γ is iteration total degree;If so, perform step 6.9;Otherwise by t+1
Value be assigned to t, return to step 6.2 sequentially performs;
Step 6.9, using F- normal forms to the t+1 times iteration topology encoder matrix W(t+1)Row vector calculated,
The result of calculation of acquisition is ranked up in descending order, selects the t+1 times iteration topology encoder matrix W(t+1)S row before middle sequence
Vector, the preceding S row vector is obtained in the t+1 times iteration topology encoder matrix W(t+1)In corresponding line number { h1,
h2,…,hs,…,hS, hsRepresent that s-th of row vector is compiled in the t+1 times iteration topology in S row vector before the sequence
Code matrix W(t+1)In line number;1≤s≤S;
Step 6.10, from the topological structure { tp '1,tp′2,…,tp′ο,…,tp′σIn selection theIndividual topology
Structure, so as to obtain the topological structure before the sequence in S row vector corresponding to s-th of row vector;So as to obtain the row
Topological structure before sequence corresponding to S row vector, and form the topological dictionary Tp={ tp " of the Aerial Images1,tp″2,…,
tp″s′,…,tp″S′};tp″s′Represent the individual topological structures of s ' in the topological dictionary Tp;1≤s′≤S′;
Step 7, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesFeature selecting vector
And the target Aerial Images I0Feature selecting vector x "0;So as to obtain the n-th image classification cnIn mnIndividual instruction
Practice Aerial ImagesThe tuple of feature selecting twoSo as to obtain the n-th image classification cnIt is middle to train figure of taking photo by plane
As InThe tuple of feature selecting twoSo as to obtain
State the N number of image classification C training Aerial Images set I tuple-set of feature selecting twoNote Represent the training Aerial Images set I of the N number of image classification C tuple-set X of feature selecting twoFSE-th
The tuple of feature selecting two, 1≤e≤E;
Step 7.1, definition selection number are ρ, and initialize ρ=1;1≤ρ≤S′;
Define the described the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beAnd initialize institute
State the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beFor sky;Define the ρ times mnIt is individual
Train Aerial ImagesFeature extraction matrix beAnd initialize
Step 7.2, choose the ρ topological structure tp " from topological dictionary Tpρ;
Step 7.3, judge the ρ topological structure tp "ρIn particle number whether be more than the mnIndividual training boat
Clap imageRegional distribution chartParticle number, if being more than, ρ+1 value is assigned to perform step 7.2 after ρ;It is no
Then, step 7.4 is performed;
Step 7.4, by the ρ topological structure tp "ρWith the ρ times mnIndividual training Aerial ImagesFeature
MatrixIn topological structure corresponding to each characteristic vector matched;If the match is successful, from the ρ times mn
Individual training Aerial ImagesFeature extraction matrixMiddle removal and the ρ topological structure tp "ρWhat the match is successful opens up
The characteristic vector corresponding to structure is flutterred, and is put into the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beIn;
Step 7.5, ρ+1 value is assigned to ρ, judges whether ρ=S ' sets up, if so, using maximum pond strategy to described
MnIndividual training Aerial ImagesFeature Choice MatrixHandled, obtain the mnIndividual training Aerial Images
Feature selecting vectorOtherwise, step 7.2- steps 7.5 are repeated;
Step 8, the topological Boosting learning models of structure:
Step 8.1, utilize the individual topological structures of s ' in the one-against-rest structure topological dictionary Tp of SVM classifier
tp″s′Disaggregated model ψs′;So as to obtain the individual disaggregated model { ψ of S ' of the topological dictionary Tp1,ψ2,...,ψs′,...,ψS′};
Step 8.2, the iterations of definition Boosting learning models are τ, and initialize iterations τ=1;
DefinitionRepresent the training Aerial Images set I of N number of image classification C during the τ times iteration feature choosing
Select two tuple-set XFSThe tuple of e-th of feature selecting twoClass weight;Then institute during the τ times iteration
State the N number of image classification C training Aerial Images set I tuple-set X of feature selecting twoFSClass weight beAnd initialize
Step 8.3, using formula (11) obtain the τ times iteration optimization disaggregated model ψ(τ);
In formula (11),Represent using probability SVM methods to the disaggregated model ψ(τ)Carry out processing acquisition
The tuple of e-th of feature selecting twoFeature selecting vectorBelong to image classificationProbability;
Step 8.4, utilize identification mistake err during formula (12) the τ times iteration of calculatingτ:
Step 8.5, utilize identification weight α during formula (13) the τ times iteration of calculatingτ:
Step 8.6, using formula (14) calculate the τ times iteration when described in e-th of class weightCorresponding classification transition power
WeightSo as to obtain E classification transition weight
Step 8.7, to the E classification transition weightIt is normalized, obtains
Class weight during+1 iteration of τ
Step 8.8, τ+1 value is assigned to τ, repeats step 8.3- steps 8.8;Until τ is equal to iterations A
Only, weight set { α is identified so as to obtain1,α2,…,αa,…,αAAnd the A iteration optimization disaggregated model { ψ(1),
ψ(2),…,ψ(a),…,ψ(A)};αaRepresent a-th of identification weight in the identification weight set;ψ(a)Represent and described a-th knowledge
The disaggregated model of the corresponding optimization of other weight;
Step 8.9, utilize the topological Boosting learning models acquisition target Aerial Images I shown in formula (15)0's
Image classification Δ;So as to realize the quick identification of target Aerial Images:
In formula (15), pa(x″0→ Δ) represent the disaggregated model ψ to described a-th optimization using probability SVM methods(a)Enter
The target Aerial Images I that row processing is obtained0Feature selecting vector x "0Belong to the probability of image classification Δ.
Compared with the prior art, the present invention has the beneficial effect that:
1st, the present invention considers the topological relation of Aerial Images, employs topological structure Classification and Identification, multi-task learning frame
The thought of frame and Boosting learning models:First, SLIC dividing methods and information entropy optimization are clustered using simple linear iteration
Method, the regional distribution chart and Region adjacency graph of each Aerial Images are built, remains the topology information in Aerial Images;Then,
The eigenmatrix of Aerial Images is obtained using Sub-Image Feature construction method, and then obtains all instructions under each image classification
Practice the topological characteristic matrix and topological characteristic projection matrix of Aerial Images;Then, realized and trained using multi-task learning framework
The topology coding square of Aerial Images, obtains topological dictionary;Feature selecting is carried out according to topological dictionary, obtains target Aerial Images
Feature selecting vector sum trains the tuple of feature selecting two of Aerial Images;Finally, the feature for training Aerial Images according to selection
Two tuples are selected to build topological Boosting learning models;The feature selecting vector of the target Aerial Images is input to described
In topological Boosting learning models, so as to obtain the classification of the target Aerial Images;Classify with conventional Aerial Images
Method is compared, and the present invention has been effectively kept the topology information in Aerial Images;More are realized using multi-task learning framework
Information sharing between business;Improve the accuracy of classification;Topological Boosting learning models are once built up, and are once calculated
The Classification and Identification of target Aerial Images quickly is obtained, the efficiency of Aerial Images classification is improved, can be obtained in real life
It is widely applied.
2nd, the present invention screens according to the principle of comentropy to the zone bit of Aerial Images segmentation gained through having gone, and removes region
First noise, the zone bit after being optimized, so as to improve the efficiency of final classification.
3rd, the present invention replaces conventional grid chart using the regional distribution chart for constructing Aerial Images so that each in Aerial Images
The topological structure of zone bit can be effectively maintained, the shortcomings that overcoming conventional grid chart, improve Aerial Images topology letter
The accuracy of breath.
4th, the eigenmatrix of subgraph is obtained using the method for Sub-Image Feature structure in the present invention, it is more representative, contain
The information of abundant Aerial Images, can include the visual signatures such as color, texture, and the geometric space that and can includes topology distribution closes
System, it is possible to increase the precision of final classification.
5th, the present invention extends Aerial Images topological characteristic and is identified using based on the characteristic matching between subgraph and subgraph
Scope, overcome in existing method because using random walk and set core method, caused by Aerial Images topological relation exist
Whether can not accurately be described, the problem of so as to cause nicety of grading low if matching.
6 compare with the Aerial Images sorting technique studied in the past, the present invention so that obtain its topological characteristic vector, fusion
Feature description of each Sub-Image Feature matrix of same Aerial Images in different topology structure, improves topological features
Precision.
7th, topological characteristic is realized in the classificatory projective measurement of different images according to topological characteristic vector in the present invention, and
The computational methods of the topological characteristic projection matrix proposed in the present invention are simple, and the mathematical formulae utilized is simply easily asked, so as to reduce
The complexity calculated.
8th, the present invention realizes the coding of final topological characteristic according to topological characteristic projection matrix, solves existing method
Aerial Images cannot distinguish between its space layout during classification for the much like image of structure, so as to reduce image point
The problem of accuracy of class.
9 compare with the Aerial Images sorting technique studied in the past, and the present invention utilizes multi-task learning framework, to multiple phases
The shared of information is realized between the image classification task of pass, realizes that topological characteristic encodes so that there is identity in topological characteristic, gram
Taken in previous methods due to a lack of considering the shared of information between inter-related task, caused by the efficiency classified it is low the problem of.
10th, the present invention obtains topological dictionary by using based on multitask topology learning feature coding, utilizes topological dictionary
To extract the Sub-Image Feature with distinctive, topological dictionary can assisting sifting go out the subgraph with identification in Aerial Images
Characteristic vector, effective classification information is extracted for disaggregated model of establishing below.
11st, the topological Boosting learning models built in the present invention, multiple Weak Classifiers are integrated into point learnt by force
Class device model, there is the characteristics of high complexity of accuracy is low, improve the precision and efficiency of learning model.
12nd, the topological Boosting learning models built in the present invention only need every time once being built up according to training image
Arbitrary target Aerial Images are inputted, is once calculated using the calculation formula of learning model, can be achieved with target Aerial Images
Quick identification, amount of calculation are small and quick.
Brief description of the drawings
Fig. 1 is present system function structure chart;
Fig. 2 is the inventive method flow chart;
Fig. 3 a are example images figures of being taken photo by plane in the inventive method;
Fig. 3 b are the regional distribution chart exemplary plots of Aerial Images in the inventive method;
Fig. 3 c are multiple area adjacency subgraph exemplary plots of Aerial Images in the inventive method;
Fig. 4 a are the topological structure exemplary plots of Aerial Images in the inventive method;
Fig. 4 b are the area adjacency subgraph exemplary plots 1 for having in the inventive method identical topological structure;
Fig. 4 c are the area adjacency subgraph exemplary plots 2 for having in the inventive method identical topological structure;
Fig. 4 d are the area adjacency subgraph exemplary plots 3 for having in the inventive method identical topological structure.
Embodiment
In the present embodiment, a kind of Aerial Images system for rapidly identifying based on multitask topology learning and method are to be used for mesh
Aerial Images class categories identification mission is marked, as shown in figure 1, a kind of Aerial Images based on multitask topology learning quickly identify
System, including:Feature construction module, topological characteristic structure module, topological characteristic projection module, multitask topology learning module,
Feature selection module and Classification and Identification module;
Feature construction module obtains several points under each image classification respectively according to set N number of image classification
Class label, and several training Aerial Images for including itself label under each image classification are obtained respectively;
Feature construction module obtains a target Aerial Images to be identified;By target Aerial Images and training Aerial Images
Form Aerial Images;Training Aerial Images are used to model;
Each Aerial Images in Aerial Images are partitioned into using simple linear iteration cluster SLIC dividing methods more
Individual zone bit, and form zone bit set;The letter of each zone bit is calculated zone bit set using probability SVM classifier method
Entropy is ceased, and whether the comentropy for weighing each zone bit is less than set information entropy threshold;Removed from zone bit set small
In the zone bit of information entropy threshold, so as to generate the zone bit set after optimization;According to the zone bit set Zhong Ge areas after optimization
Positional information of the domain member in Aerial Images, and one generated by the adjacent zone bit of any two of positional information acquisition
Bar connects side, builds the regional distribution chart of each Aerial Images in Aerial Images;
Several area adjacency subgraphs are generated by the regional distribution chart of each Aerial Images;By area adjacency subgraph Zhong Ge areas
The positional information of domain member and connection side form the respective topological structure of area adjacency subgraph;It is made up of all area adjacency subgraphs
The Region adjacency graph of each Aerial Images;
It is sub to several area adjacencies of each Aerial Images using Sub-Image Feature construction method and core principle component analysis method
Figure is handled, and obtains the characteristic vector of several area adjacency subgraphs of each Aerial Images, each area adjacency respectively
The characteristic vector of figure and the topological structure of respective corresponding area adjacency subgraph correspond;By some of each Aerial Images
The characteristic vector of individual area adjacency subgraph forms the eigenmatrix of respective Aerial Images in Aerial Images;The feature square of Aerial Images
Battle array includes the eigenmatrix of target Aerial Images and the eigenmatrix of training Aerial Images;
Topological characteristic builds module will have identical topological structure characteristic vector in the eigenmatrix for training Aerial Images
Fusion treatment is carried out, obtains the topological characteristic matrix of training Aerial Images;So as to obtain all training under each image classification
The topological characteristic matrix of Aerial Images;
Topological characteristic projection module is trained itself label of Aerial Images according to several tag along sorts and each and opened up
Eigenmatrix is flutterred, obtains the topological characteristic projection matrix of the training Aerial Images under each Aerial Images classification;
Multitask topology learning module is projected using multi-task learning framework and principal component analytical method to topological characteristic
Matrix is iterated, so as to obtain the topological encoder matrix of training Aerial Images;Row using F- normal forms to topological encoder matrix
Vector is calculated, and the result of calculation of acquisition is ranked up in descending order, the topological structure before selection corresponding to S row vector, structure
Into the topological dictionary of Aerial Images;
Feature selection module is according to each characteristic vector in the eigenmatrix of each topological structure and Aerial Images in topological dictionary
Corresponding topological structure is matched, if the match is successful, by the characteristic vector that the match is successful in the eigenmatrix of Aerial Images
Form the feature selecting vector of Aerial Images;Aerial Images feature selecting vector include target Aerial Images feature selecting to
The feature selecting vector of amount and training Aerial Images;By the feature selecting vector and training Aerial Images of each training Aerial Images
Each corresponding image classification, form the tuple of feature selecting two of each training Aerial Images;
Classification and Identification module builds multiple disaggregated models to topological dictionary using SVM classifier;And taken photo by plane figure according to training
The tuple of feature selecting two of picture obtains multiple disaggregated models of optimization using probability SVM methods;By the multiple disaggregated models optimized
Build topological Boosting learning models;The feature selecting vector of target Aerial Images is input to topological Boosting study mould
In type, so as to obtain the classification of target Aerial Images.
In the present embodiment, it is C={ c to define N number of image classification1,c2,…,cn,…,cN};cnRepresent n-th image classification;
Several tag along sorts are included in each image classification;N-th of classification c of notenTag along sort beknRepresent tag number, 1≤kn≤Kn;Represent kthnIndividual tag along sort;KnRepresent n-th
Individual image classification cnThe sum of middle tag along sort, Kn∈ [10,50], in specific implementation, Kn=10;Then N number of image classification C point
Class tag set is T={ T1,T2,…,Tn,…,TN};Remember that κ is the sum of all tag along sorts in tag along sort set T, and have
Remember n-th image classification cnIn comprising the training Aerial Images of itself label be Represent n-th image classification cnIn mnIndividual training Aerial Images,Represent
N-th image classification cnIn mnIndividual training Aerial ImagesItself tag set, 1≤mn≤Mn;MnRepresent n-th image
Classify cnIn it is all training Aerial Images sums, for example, Mn∈[5000,10000];Then N number of image classification C training is taken photo by plane
Image collection is I={ I1,I2,…,In,…,IN, i.e., as shown in Fig. 2 havingRemember that target Aerial Images are
I0;
As shown in Fig. 2 a kind of recognition methods of the Aerial Images system for rapidly identifying based on multitask topology learning be by
Following steps are carried out:
Step 1, structure n-th image classification cnIn mnIndividual training Aerial ImagesRegional distribution chart
So as to obtain n-th image classification cnIn it is all training Aerial Images regional distribution chartsSo as to obtain N number of image classification C training Aerial Images collection
Close I area distribution set of graphs RAG={ RAG1,RAG2,…,RAGn,…,RAGN, i.e., as shown in Fig. 2 havingAnd target Aerial Images I0Regional distribution chart RAG0;
Step 1.1, cluster SLIC dividing methods using simple linear iteration n-th image is classified cnIn mnIndividual training
Aerial ImagesIt is divided intoIndividual zone bit, obtain mnIndividual training Aerial ImagesZone bit setRepresent n-th image classification cnIn mnIt is individual to train figure of taking photo by plane
Picture Individual zone bit;MnIndividual training Aerial ImagesIn include theIndividual zone bit's
Positional information;Specific simple linear iteration cluster SLIC dividing methods refer to document《SLIC Superpixels
Compared to State-of-the-art Superpixel Methods》In introduction;Simple linear iteration clusters SLIC
Dividing method is a kind of super-pixel segmentation based on clustering algorithm, and by LAB spaces and x, y pixel coordinate table, totally 5 dimension spaces come
Calculate;The method not only can with Segmentation of Color Images, can also compatible segmentation gray-scale map, but also can taking human as setting need
The quantity in the region of segmentation;The advantages of simple linear iteration cluster SLIC dividing methods, is:On the one hand, efficiency is improved, and
The subregion of division has been effectively maintained the information of source images, and the degree of accuracy is high;On the other hand, amount of calculation is small, and time cost is with super
Number of pixels linear increase;In this implementation, by set different simple linear iteration cluster SLIC serious forgiveness parameter 0.1,
0.2,0.3,0.4,0.5 }, repeated segmentation is carried out to Aerial Images;
Step 1.2, utilize probability SVM classifier method calculating mnIndividual zone bit setInIndividual zone bitBelong to n-th image classification cnProbabilitySo as to obtainIndividual zone bitBelong to image point
The probability each classified in class C;Specific probability SVM classifier method refers to document《Probabilistic Outputs
for Support Vector Machines and Comparisons to Regularized Likelihood Method》
In introduction;Probability SVM classifier method is the support vector method of posterior probability, is the extension of standard SVM classifier and pushes away
Extensively;All it is that Nonlinear Mapping is first selected according to priori in advance, input vector is mapped to a high dimensional feature sky using it
Between, construct optimal hyperlane in this space;In SVM, training sample always has clear and definite classification information, and for one
A little uncertain problems are simultaneously incorrect;Probability SVM is then inspired by Bayes decision rule, using the posterior probability of sample come table
Show this uncertainty, Bayes decision rule is combined with SVM, establishes posterior probability support vector machine method, to solve
Certain problem by no means;The present invention can improve to obtain nicety of grading using the method;
Step 1.3, using formula (1) calculate theIndividual zone bitComentropySo as to obtain mnIt is individual
Zone bit setIn each zone bit comentropy:
Step 1.4, judge comentropyWhether information entropy threshold δ is less than, if being less than, from mnIndividual region metaset
CloseThe middle zone bit removed less than information entropy threshold δ;So as to obtain mnIndividual training Aerial ImagesOptimization after area
Domain member setδ∈[0.1,0.5];δ can be adjusted using step-length as 0.05;
Step 1.5, with mnIndividual training Aerial ImagesOptimization after zone bit setIn each zone bit position
Central point is put as particle, the particle of the adjacent zone bit of any two in each zone bit is attached using adjacent side, from
And obtain mnIndividual training Aerial ImagesRegional distribution chart
For example, Fig. 3 a are an Aerial Images example;After shown in Fig. 3 b being then the optimization in Fig. 3 a corresponding to Aerial Images
The regional distribution chart that is obtained of zone bit set;
Step 2, structure n-th image classification cnIn mnIndividual training Aerial ImagesRegion adjacency graph
From mnIndividual training Aerial ImagesRegional distribution chartMiddle exhaustive several particles for including connection side,
M is formed by several particles and corresponding connection sidenIndividual training Aerial ImagesArea adjacency subgraphRepresent theIndividual area adjacency subgraph, by each in area adjacency subgraph
The positional information of zone bit and connection side form the topological structure of area adjacency subgraph;Represent mnIndividual training Aerial ImagesArea adjacency subgraph sum;By mnIndividual training Aerial ImagesArea adjacency subgraphForm n-th image classification cnIn mnIndividual training Aerial ImagesRegion it is adjacent
Map interlinkingAs shown in Figure 3 c, the multiple area adjacency subgraphs obtained by regional distribution chart in Fig. 3 b;It is only portion shown in Fig. 3 c
Subregion abuts subgraph example;So as to obtain n-th image classification cnIn it is all training Aerial Images Region adjacency graphsSo as to obtain N number of image classification C training Aerial Images set I area adjacency
Set of graphs G={ G1,G2,…,Gn,…,GN, i.e., as shown in Fig. 2 havingAnd target Aerial Images I0
Regional distribution chart G0;
Step 3, obtain n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixSo as to obtain
N-th image classification cnTraining Aerial Images eigenmatrixSo as to obtain N
Individual image classification C training Aerial Images set I eigenmatrix set X={ X1,X2,…,Xn,…,XN, i.e., such as Fig. 2 institutes
Show haveAnd target Aerial Images I0Eigenmatrix X0;
Step 3.1, to mnIndividual Aerial ImagesRegion adjacency graphMiddle particle number identical area adjacency
FigureWithSub-Image Feature matrix is respectively obtained using Sub-Image Feature construction methodWith Specific Sub-Image Feature construction method bibliography《Probabilistic Graphlet Transfer
for Photo Cropping》;Sub-Image Feature construction method utilizes classical color of image feature extracting method and textural characteristics
Extracting method obtains the color and texture visual signature of each area adjacency subgraph, and extracts its geometry feature, to each
Area adjacency subgraph, these three features are sequentially connected constitutive characteristic matrix;That is Sub-Image Feature matrix;
Step 3.2, utilize formula (2) acquisition Sub-Image Feature matrixWithGolub-Werman distances
In formula (2),WithSub-Image Feature matrix is represented respectivelyWithOrthogonal basis;|
|·||2Represent 2- normal forms;
Step 3.3, utilize formula (3) zoning adjoining subgraphWithGaussian kernel
In formula (3),Gauss nuclear parameter is represented,
Step 3.4, repeat step 3.1 arrive step 3.3, so as to obtain mnIndividual Region adjacency graphIn all there is phase
The Gaussian kernel of the area adjacency subgraph of homologous pints;
Step 3.5, using core principle component analysis method (Kernel PCA methods) obtain mnIndividual Aerial ImagesFeature
MatrixForIndividual area adjacency subgraphFeature to
Amount,D isIndividual characteristic vectorDimension;TheIndividual area adjacency subgraphCharacteristic vectorWithIndividual area adjacency subgraphTopological structure correspond;Specific core principle component analysis method (Kernel
PCA methods) refer to document《Kernel PCA for Feature Extraction and De-Noising in
Nonlinear Regression》;A kind of core principle component analysis method (Kernel PCA methods) nonlinear pca method
(PCA methods), its main thought are that input vector is mapped into a higher-dimension by the Nonlinear Mapping that certain is selected in advance
Among linear character space, pivot composition then is calculated using PCA in High-dimensional Linear feature space, it has
The advantages that nonlinear characteristic of data can effectively be caught, do not required the distribution situations of the data in luv space;It is based on
This advantage, the present invention can utilize the method by the very high variables transformations of script correlation into being mutually independent or incoherent
Variable, the characteristic vector mutual independence for obtaining each Region adjacency graph are stronger;
Step 4, obtain n-th image classification cnIn mnIndividual training Aerial ImagesTopological characteristic vectorSo as to
Obtain n-th image classification cnThe topological characteristic matrix of middle training Aerial ImagesFrom
And obtain N number of image classification C training Aerial Images set I topological characteristic set of matrices X '={ X1′,X′2,…,X′n,…,
X′N, i.e., as shown in Fig. 2 having
Step 4.1, retain n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixIn own
In topological structure corresponding to characteristic vector topological structure differ and particle number be no more than upper limit ν topological structure, from
And obtain n-th image classification cnIn mnIndividual training Aerial ImagesTopological structure setSo as to obtain n-th of figure
As classification cnTraining Aerial Images topological structure setHandled using unionI.e. to topological structure set { tp1,tp2,…,tpn,…,tpNIn all topological structure do union operation:
Retain different topological structures, one is only retained to identical topological structure;The different Aerial Images productions as shown in Fig. 4 b, 4c and 4d
Raw multiple area adjacency subgraphs have identical topological structure, and identical topological structure is as shown in institute Fig. 4 a;To such topology
When structure does union processing, it is only necessary to retain a topological structure, it is not necessary to repeat to retain;So as to obtain N number of image point
Class C training Aerial Images set I topological structure set tp;
Step 4.2, training Aerial Images set I to N number of image classification C topological structure set tp in each topology
Structure is numbered, and obtains σ topological structure and is designated as { tp '1,tp′2,…,tp′ο,…,tp′σ};tp′οRepresent the ο topology knot
Structure;1≤ο≤σ;In the present embodiment, as ν=7, σ values are 160 or so;
Step 4.3, by mnIndividual Aerial ImagesEigenmatrixIn topological structure corresponding to each characteristic vector
With the ο topological structure tp 'οIt is compared, selects the characteristic vector with identical topological structure and summed and take average place
Reason;Obtain mnIndividual Aerial ImagesIn the ο topological structure tp 'οOn topology fusion vector
So as to obtain mnIndividual Aerial ImagesIn σ topological structure { tp1′,tp2′,…,tp′ο,…,tp′σOn topology merge to
Amount
Step 4.3, by mnIndividual Aerial ImagesIn σ topological structure { tp '1,tp′2,…,tp′ο,…,tp′σOn
Topology fusion vectorEnd to end fusion treatment is carried out, obtains mnIndividual instruction
Practice Aerial ImagesTopological characteristic vectorAnd haveD σ=d × σ;
Step 5, the topological projection matrix set M={ M for training Aerial Images set I for obtaining N number of image classification C1,
M2,…,Mn,…,MN}:
Step 5.1, n-th image classification c is obtained using formula (4)nMiddle training Aerial Images InTopological transitions matrix
In formula (4),For unit matrix,Represent MnIndividual element is all 1
One dimensional vector,
Step 5.2, n-th image classification c is obtained using formula (5)nIn mnIndividual training Aerial ImagesKthnIndividual mark
Sign elementSo as to obtain n-th image classification cnIn mnIndividual training Aerial ImagesLabel vectorSo as to obtain n-th image classification cnTraining boat
Clap image InLabel matrixAnd have
Step 5.3, n-th image classification c is obtained using formula (6)nMiddle training Aerial Images InLabel measurement oriental matrix
Fn,
Fn=Yn[(Yn)TYn]-1/2 (6)
Step 5.4, n-th image classification c is obtained using formula (7)nMiddle training Aerial Images InTopological projection matrix Mn,So as to obtain N number of image classification C training Aerial Images set I topological projection matrix set M={ M1,
M2,…,Mn,…,MN, i.e., as shown in Fig. 2 having
Step 6, multitask topology learning feature coding:
Step 6.1, definition iterations are t;Initialize iterations t=1;
And the topological encoder matrix of the t times iteration is when initializing the t times iterationAnd n-th of topological coded sub-matrices when having the t times iterationIn each member
Element is obtained by random function;
Step 6.2, using formula (8) obtain the t times iteration topological encoder matrix be W(t)Code weight matrix D(t)'s
V row v column elementsCode weight matrix D during so as to obtain the t times iteration(t):
In formula (8),The topological encoder matrix for representing the t times iteration is W(t)V every trades vector, code weight matrix
D(t)For diagonal matrix,
Step 6.3, definition tasking learning number are l;1≤l≤N;And initialization task study number l=1;
Step 6.4, the weight selection matrix that the t times iteration l subtask learns is obtained using formula (9)In v rows
V column elementsSo as to obtain the weight selection matrix of the t times iteration l subtasks study
In formula (9),Represent l-th of topological coded sub-matrices during the t times iterationV row vectors, weight choosing
Select matrixFor diagonal matrix,
Step 6.5, from topological characteristic projection matrix set M={ M1,M2,…,Mn,…,MNIn to extract l-th of topology special
Levy projection matrix Ml;The code optimization matrix of the t times iteration l subtasks study is obtained using formula (10)
In formula (10), λ presentation code Optimal Parameters, λ ∈ [0,0.1];μ presentation code internal coordination parameters, μ ∈ [0,
0.5];For example, λ=0.01, μ=0.3;
Step 6.6, note KlFor l-th of image classification c in N number of image classification ClThe sum of middle tag along sort;Using it is main into
Analysis method (PCA methods) is divided to obtain the code optimization matrix of the t times iteration l subtasks studyMultiple characteristic values and
The characteristic vector corresponding with multiple characteristic values, multiple characteristic values are sorted from small to large, and select preceding KlIndividual characteristic value institute is right
N-th of topological coded sub-matrices when the characteristic vector answered is as the t times iterationPCA (PCA methods) is one
Data analysing method of the kind based on second-order statisticses, one group of given correlated variables is changed into another group of not phase by it by linear transformation
The variable of pass, the order that these new variables successively decrease successively according to variance are arranged, and the population variance of variable is kept in mathematic(al) manipulation
It is constant;The main purpose of PCA is the most of variable gone to explain in original data with less variable;We
Selected using the method than code optimization matrix in faceMiddle characteristic vector number is few but can explain most of code optimization matrixSeveral new vectors of middle characteristic vector;
Step 6.7, judge whether l=N sets up, if so, n times tasking learning is then completed, so as to obtain the t+1 times iteration
Topological encoder matrixAnd perform step 6.8;Otherwise l+1 value is assigned to l, weight
Step 6.4- steps 6.7 are performed again;
Step 6.8, judge whether t=Γ set up, Γ is iteration total degree, for example, Γ=1000;If so, perform step
Rapid 6.9;Otherwise t+1 value is assigned to t, return to step 6.2 sequentially performs;
Step 6.9, using F- normal forms to the t+1 times iteration topology encoder matrix W(t+1)Row vector calculated, obtain
Result of calculation be ranked up in descending order, select the t+1 times iteration topology encoder matrix W(t+1)S row vector, is obtained before middle sequence
S row vector is in the t+1 times iteration topology encoder matrix W before obtaining(t+1)In corresponding line number { h1,h2,…,hs,…,hS,
hsRepresent before sequence that s-th of row vector is in the t+1 times iteration topology encoder matrix W in S row vector(t+1)In line number;1≤
s≤S;The t+1 times iteration topology encoder matrix W(t+1)In line number started counting up with 1;
Step 6.10, from topological structure { tp '1,tp′2,…,tp′ο,…,tp′σIn selection theIndividual topology knot
Structure, so as to the topological structure corresponding to s-th of row vector in S row vector before being sorted;So as to before being sorted S row to
The corresponding topological structure of amount, due to there may be identical topological structure in the topological structure corresponding to preceding S row vector, this
Only retain different topological structures in implementation, and form the topological dictionary Tp={ tp " of Aerial Images1,tp″2,…,tp″s′,…,
tp″S′};tp″s′Represent the individual topological structures of s ' in topological dictionary Tp;1≤s′≤S′;
Step 7, obtain n-th image classification cnIn mnIndividual training Aerial ImagesFeature selecting vectorWith
And target Aerial Images I0Feature selecting vector x "0;So as to obtain n-th image classification cnIn mnIndividual training Aerial ImagesThe tuple of feature selecting twoSo as to obtain n-th image classification cnMiddle training Aerial Images InFeature selecting
Two tuplesSo as to obtain N number of image classification C instruction
Practice the Aerial Images set I tuple-set of feature selecting two
NoteI.e. as shown in Fig. 2 havingRepresent N number of image classification C training Aerial Images set I feature selecting binary
Group set XFSThe tuple of e-th of feature selecting two, 1≤e≤E;
Step 7.1, definition selection number are ρ, and initialize ρ=1;1≤ρ≤S′;
Define the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beAnd initialize the ρ times mn
Individual training Aerial ImagesFeature Choice Matrix beFor sky;Define the ρ times mnIndividual training Aerial ImagesSpy
Sign extracts matrixAnd initialize
Step 7.2, choose the ρ topological structure tp " from topological dictionary Tpρ;
Step 7.3, judge the ρ topological structure tp 'ρ' in particle number whether be more than mnIndividual training Aerial ImagesRegional distribution chartParticle number, if being more than, ρ+1 value is assigned to perform step 7.2 after ρ;Otherwise, hold
Row step 7.4;
Step 7.4, by the ρ topological structure tp "ρWith the ρ times mnIndividual training Aerial ImagesEigenmatrix
In topological structure corresponding to each characteristic vector matched;If the match is successful, from the ρ times mnIndividual training Aerial ImagesFeature extraction matrixMiddle removal and the ρ topological structure tp "ρThe feature corresponding to topological structure that the match is successful
Vector, and it is put into the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beIn;
Step 7.5, ρ+1 value is assigned to ρ, judges whether ρ=S ' sets up, if so, using maximum pond strategy to mn
Individual training Aerial ImagesFeature Choice MatrixHandled, obtain mnIndividual training Aerial ImagesFeature
Selection vectorOtherwise, step 7.2- steps 7.5 are repeated;Using maximum pond strategy i.e. by often row retains maximum in matrix
It is worth element and forms a new vector;
Step 8, the topological Boosting learning models of structure:
Boosting methods are a kind of methods for improving the weak typing algorithm degree of accuracy, by constructing an anticipation function
Series, they are then combined into an anticipation function in some way;The discrimination of each single grader is not necessarily
It is very high, but the result after their joints has very high discrimination, and this implementation uses the method, it is possible to increase the identification of disaggregated model
Rate;
Step 8.1, the is built in topological dictionary Tp using the one-against-rest (One-Against-All) of SVM classifier
The individual topological structure tp " of s 's′Disaggregated model ψs′;So as to obtain the individual disaggregated model { ψ of topological dictionary Tp S '1,ψ2,...,
ψs′,...,ψS′};The one-to-many methods of one-against-rest (one-against-all) of SVM classifier, successively some during training
The sample of classification is classified as one kind, and other remaining samples are classified as another kind of, and the sample of such k classification has just constructed k
SVM.Unknown sample is categorized as to that class with maximum classification function value during classification;Realized and taken photo by plane using the method in the present invention
The classification of image, it is possible to achieve the multi-class identification of Aerial Images classification;
Step 8.2, the iterations of definition Boosting learning models are τ, and initialize iterations τ=1;
DefinitionRepresent the training Aerial Images set I of the N number of image classification C during the τ times iteration tuple of feature selecting two
Set XFSThe tuple of e-th of feature selecting twoClass weight;Then during the τ times iteration N number of image classification C training
The Aerial Images set I tuple-set X of feature selecting twoFSClass weight beAnd initialize
Step 8.3, using formula (11) obtain the τ times iteration optimization disaggregated model ψ(τ);
In formula (11),Represent using probability SVM methods to disaggregated model ψ(τ)Carry out handling the of acquisition
The e tuple of feature selecting twoFeature selecting vectorBelong to image classificationProbability;With general in step 1.2
Rate SVM classifier explains that equally specific probability SVM classifier method refers to document《Probabilistic Outputs
for Support Vector Machines and Comparisons to Regularized Likelihood Method》
In introduction, the present invention can improve to obtain nicety of grading using the method;
Step 8.4, utilize identification mistake err during formula (12) the τ times iteration of calculatingτ:
Step 8.5, utilize identification weight α during formula (13) the τ times iteration of calculatingτ:
Step 8.6, utilize e-th of class weight during formula (14) the τ times iteration of calculatingCorresponding classification transition weight;So as to obtain E classification transition weight
Step 8.7, to E classification transition weightIt is normalized, acquisition τ+
Class weight during 1 iteration
Step 8.8, τ+1 value is assigned to τ, repeats step 8.3- steps 8.8;Until τ is equal to iterations A
Only, for example, A=1000, weight set { α is identified so as to obtain1,α2,…,αa,…,αAAnd A iteration optimization classification mould
Type { ψ(1),ψ(2),…,ψ(a),…,ψ(A)};αaRepresent a-th of identification weight in identification weight set;ψ(a)Represent and a-th of knowledge
The disaggregated model of the corresponding optimization of other weight;
Step 8.9, utilize the topological Boosting learning models acquisition target Aerial Images I shown in formula (15)0Image
Classification Δ;So as to realize the quick identification of target Aerial Images:
In formula (15), pa(x″0→ Δ) represent the disaggregated model ψ to a-th of optimization using probability SVM methods(a)Located
The obtained target Aerial Images I of reason0Feature selecting vector x "0Belong to the probability of image classification Δ;With probability in step 1.2
SVM classifier explains that equally specific probability SVM classifier method sees bibliography《Probabilistic Outputs for
Support VectorMachines and Comparisons to Regularized LikelihoodMethod》In Jie
Continue, the present invention can improve to obtain nicety of grading using the method.
More than, it is only a kind of present invention preferably embodiment, other researchers, completely can be at other according to above
In association area, such as image-region target, image scene, picture position Classification and Identification etc., obtain identical effect.It must say
It is bright, any one skilled in the art the invention discloses technical scope in, the technique according to the invention
Scheme and its inventive concept are subject to equivalent substitution or relevant parameter changes, and should all be included within the scope of the present invention.
Claims (2)
1. a kind of Aerial Images system for rapidly identifying based on multitask topology learning, it is characterized in that including:Feature construction module,
Topological characteristic structure module, topological characteristic projection module, multitask topology learning module, feature selection module and Classification and Identification mould
Block;
The feature construction module obtains several points under each image classification respectively according to set N number of image classification
Class label, and several training Aerial Images for including itself label under each image classification are obtained respectively;
The feature construction module obtains a target Aerial Images to be identified;By the target Aerial Images and the training
Aerial Images form Aerial Images;
Each Aerial Images in the Aerial Images are partitioned into using simple linear iteration cluster SLIC dividing methods more
Individual zone bit, and form zone bit set;Each zone bit is calculated using probability SVM classifier method to the zone bit set
Comentropy, and whether the comentropy for weighing each zone bit is less than set information entropy threshold;From the zone bit set
The middle zone bit removed less than described information entropy threshold, so as to generate the zone bit set after optimization;After the optimization
Positional information of each zone bit in the Aerial Images in zone bit set, and any two phase obtained by positional information
The connection side that adjacent zone bit is generated, build the regional distribution chart of each Aerial Images in the Aerial Images;
Several area adjacency subgraphs are generated by the regional distribution chart of each Aerial Images;By in the area adjacency subgraph
The positional information of each zone bit and connection side form the respective topological structure of area adjacency subgraph;By all area adjacencies
Subgraph forms the Region adjacency graph of each Aerial Images;
It is sub to several area adjacencies of each Aerial Images using Sub-Image Feature construction method and core principle component analysis method
Figure is handled, and obtains the characteristic vector of several area adjacency subgraphs of each Aerial Images, each area adjacency respectively
The characteristic vector of figure and the topological structure of respective corresponding area adjacency subgraph correspond;By each Aerial Images
The characteristic vector of several area adjacency subgraphs forms the eigenmatrix of respective Aerial Images in the Aerial Images;It is described to take photo by plane
The eigenmatrix of image includes the eigenmatrix of target Aerial Images and the eigenmatrix of training Aerial Images;
The topological characteristic structure module will have identical topological structure feature in the eigenmatrix of the training Aerial Images
Vector carries out fusion treatment, obtains the topological characteristic matrix of the training Aerial Images;So as to obtain each image classification
Under all training Aerial Images topological characteristic matrix;
The topological characteristic projection module is according to itself of several described tag along sorts and each training Aerial Images
Label and topological characteristic matrix, obtain the topological characteristic projection square of the training Aerial Images under each Aerial Images classification
Battle array;
The multitask topology learning module is using multi-task learning framework and principal component analytical method to the topological characteristic
Projection matrix is iterated, so as to obtain the topological encoder matrix of the training Aerial Images;Using F- normal forms to the topology
The row vector of encoder matrix is calculated, and the result of calculation of acquisition is ranked up in descending order, before selection corresponding to S row vector
Topological structure, form the topological dictionary of the Aerial Images;
The feature selection module is according to each in the eigenmatrix of each topological structure in the topological dictionary and the Aerial Images
Topological structure is matched corresponding to characteristic vector, if the match is successful, by the eigenmatrix of the Aerial Images match into
The characteristic vector of work(forms the feature selecting vector of the Aerial Images;The feature selecting vector of the Aerial Images includes target
The feature selecting vector of the feature selecting vector sum training Aerial Images of Aerial Images;Selected by the feature of each training Aerial Images
Vectorial and described training Aerial Images each corresponding image classification is selected, forms the feature selecting two for each training Aerial Images
Tuple;
The Classification and Identification module builds multiple disaggregated models to the topological dictionary using SVM classifier;And according to the instruction
The tuple of feature selecting two for practicing Aerial Images obtains multiple disaggregated models of optimization using probability SVM methods;By the optimization
Multiple disaggregated models build topological Boosting learning models;The feature selecting vector of the target Aerial Images is input to institute
State in topological Boosting learning models, so as to obtain the classification of the target Aerial Images.
A kind of 2. quick knowledge of Aerial Images system for rapidly identifying based on multitask topology learning using described in claim 1
Other method, it is characterized in that,
It is C={ c to define N number of image classification1,c2,…,cn,…,cN};cnRepresent n-th image classification;In each image classification
Include several tag along sorts;Remember n-th of classification cnTag along sort beknRepresent
Tag number, 1≤kn≤Kn;Represent kthnIndividual tag along sort;KnRepresent the n-th image classification cnMiddle tag along sort
Sum;Then the tag along sort collection of N number of image classification C is combined into T={ T1,T2,…,Tn,…,TN};Note κ is the contingency table
The sum of all tag along sorts in set T is signed, and is had
Remember the n-th image classification cnIn comprising the training Aerial Images of itself label be Represent n-th image classification cnIn mnIndividual training Aerial Images,Represent
The n-th image classification cnIn mnIndividual training Aerial ImagesItself tag set, 1≤mn≤Mn;MnRepresent described
N image classification cnIn it is all training Aerial Images sums;Then the training Aerial Images collection of N number of image classification C is combined into I
={ I1,I2,…,In,…,IN};Remember that target Aerial Images are I0;
The method for quickly identifying is to carry out as follows:
Step 1, structure the n-th image classification cnIn mnIndividual training Aerial ImagesRegional distribution chart
So as to obtain the n-th image classification cnIn it is all training Aerial Images regional distribution chartsTaken photo by plane figure so as to obtain the training of N number of image classification C
Image set closes I area distribution set of graphs RAG={ RAG1,RAG2,…,RAGn,…,RAGN};And the target Aerial Images I0
Regional distribution chart RAG0;
Step 1.1, using simple linear iteration cluster SLIC dividing methods by the n-th image classify cnIn mnIndividual training
Aerial ImagesIt is divided intoIndividual zone bit, obtain the mnIndividual training Aerial ImagesZone bit set Represent the n-th image classification cnIn mnIndividual training is taken photo by plane
Image Individual zone bit;The mnIndividual training Aerial ImagesIn include described theIndividual area
Domain memberPositional information;
Step 1.2, utilize the probability SVM classifier method calculating mnIndividual zone bit setInIndividual zone bitBelong to n-th image classification cnProbabilitySo as to obtain describedIndividual zone bitBelong to institute
State the probability each classified in image classification C;
Step 1.3, using formula (1) calculate described theIndividual zone bitComentropySo as to obtain the mn
Individual zone bit setIn each zone bit comentropy:
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Step 1.4, judge described information entropyWhether information entropy threshold δ is less than, if being less than, from the mnIndividual zone bit
SetThe middle zone bit removed less than described information entropy threshold δ;So as to obtain the mnIndividual training Aerial Images's
Zone bit set after optimization
Step 1.5, with the mnIndividual training Aerial ImagesOptimization after zone bit setIn each zone bit position
Central point is put as particle, the particle of the adjacent zone bit of any two in each zone bit is connected using adjacent side
Connect, so as to obtain the mnIndividual training Aerial ImagesRegional distribution chart
Step 2, structure the n-th image classification cnIn mnIndividual training Aerial ImagesRegion adjacency graph
From the mnIndividual training Aerial ImagesRegional distribution chartMiddle exhaustive several particles for including connection side,
The m is formed by several described particles and corresponding connection sidenIndividual training Aerial ImagesArea adjacency subgraphRepresent theIndividual area adjacency subgraph, by the area adjacency subgraph
In each zone bit positional information and connection side form the topological structure of the area adjacency subgraph;Represent the mnIt is individual
Train Aerial ImagesArea adjacency subgraph sum;By the mnIndividual training Aerial ImagesArea
Domain abuts subgraphForm the n-th image classification cnIn mnIt is individual to train figure of taking photo by plane
PictureRegion adjacency graphSo as to obtain the n-th image classification cnIn it is all training Aerial Images area adjacencies
FigureSo as to obtain the training Aerial Images set I of N number of image classification C area
Domain adjoining set of graphs G={ G1,G2,…,Gn,…,GN};And the target Aerial Images I0Regional distribution chart G0;
Step 3, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixSo as to obtain
The n-th image classification cnTraining Aerial Images eigenmatrixSo as to obtain
Obtain the training Aerial Images set I of N number of image classification C eigenmatrix set X={ X1,X2,…,Xn,…,XN};And
The target Aerial Images I0Eigenmatrix X0;
Step 3.1, to the mnIndividual Aerial ImagesRegion adjacency graphMiddle particle number identical area adjacency subgraphWithSub-Image Feature matrix is respectively obtained using Sub-Image Feature construction methodWith
Step 3.2, utilize formula (2) the acquisition Sub-Image Feature matrixWithGolub-Werman distances
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In formula (2),WithThe Sub-Image Feature matrix is represented respectivelyWithOrthogonal basis;|
|·||2Represent 2- normal forms;
Step 3.3, utilize formula (3) the calculating area adjacency subgraphWithGaussian kernel
In formula (3),Represent Gauss nuclear parameter;
Step 3.4, repeat step 3.1 arrive step 3.3, so as to obtain the mnIndividual Region adjacency graphIn it is all have it is identical
The Gaussian kernel of the area adjacency subgraph of particle;
Step 3.5, the m obtained using core principle component analysis methodnIndividual Aerial ImagesEigenmatrix For describedIndividual area adjacency subgraphFeature to
Amount,D isIndividual characteristic vectorDimension;DescribedIndividual area adjacency subgraphSpy
Sign vectorWith describedIndividual area adjacency subgraphTopological structure correspond;
Step 4, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesTopological characteristic vector
So as to obtain the n-th image classification cnThe topological characteristic matrix of middle training Aerial ImagesSo as to obtain the training Aerial Images set I of N number of image classification C topology
Eigenmatrix set X '={ X1′,X′2,…,X′n,…,X′N};
Step 4.1, retain the n-th image classification cnIn mnIndividual training Aerial ImagesEigenmatrixIn own
In topological structure corresponding to characteristic vector topological structure differ and particle number be no more than upper limit ν topological structure, from
And obtain the n-th image classification cnIn mnIndividual training Aerial ImagesTopological structure setSo as to obtain
State n-th image classification cnTraining Aerial Images topological structure setUtilize
Union processingSo as to obtain the training Aerial Images set I of N number of image classification C topological structure set
tp;
Step 4.2, training Aerial Images set I to N number of image classification C topological structure set tp in each topology
Structure is numbered, and obtains σ topological structure and is designated as { tp1′,tp2′,…,tp′ο,…,tp′σ};tp′οRepresent the ο topology knot
Structure;1≤ο≤σ;
Step 4.3, by the mnIndividual Aerial ImagesEigenmatrixIn topological structure corresponding to each characteristic vector
With the ο topological structure tp 'οIt is compared, selects the characteristic vector with identical topological structure and summed and made even
Handle;Obtain the mnIndividual Aerial ImagesIn the ο topological structure tp 'οOn topology fusion vector So as to obtain the mnIndividual Aerial ImagesIn the σ topological structure { tp1′,tp2′,…,tp
′ο,…,tp′σOn topology fusion vector
Step 4.3, by the mnIndividual Aerial ImagesIn the σ topological structure { tp1′,tp2′,…,tp′ο,…,tp′σ}
On topology fusion vectorEnd to end fusion treatment is carried out, described in acquisition
MnIndividual training Aerial ImagesTopological characteristic vectorAnd haveD σ=d × σ;
Step 5, the topological projection matrix set M={ M for training Aerial Images set I for obtaining N number of image classification C1,
M2,…,Mn,…,MN}:
Step 5.1, classified c using formula (4) the acquisition n-th imagenMiddle training Aerial Images InTopological transitions matrix
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Practice Aerial Images InLabel matrixAnd have
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Fn=Yn[(Yn)TYn]-1/2 (6)
Step 5.4, classified c using formula (7) the acquisition n-th imagenMiddle training Aerial Images InTopological projection matrix Mn,So as to obtain the training Aerial Images set I of N number of image classification C topological projection matrix set M=
{M1,M2,…,Mn,…,MN};
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Step 6, multitask topology learning feature coding:
Step 6.1, definition iterations are t;Initialize the iterations t=1;
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Step 6.2, the topological encoder matrix for obtaining using formula (8) the t times iteration are W(t)Code weight matrix D(t)'s
V row v column elementsCode weight matrix D during so as to obtain the t times iteration(t):
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ElementSo as to obtain the weight selection matrix of the t times iteration l subtasks study
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Select matrixFor diagonal matrix,
Step 6.5, from the topological characteristic projection matrix set M={ M1,M2,…,Mn,…,MNL-th of topological characteristic of middle extraction
Projection matrix Ml;The code optimization matrix of the t times iteration l subtasks study is obtained using formula (10)
<mrow>
<msubsup>
<mi>U</mi>
<mi>l</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>=</mo>
<msub>
<mi>M</mi>
<mi>l</mi>
</msub>
<mo>+</mo>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<msubsup>
<mi>D</mi>
<mi>l</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>+</mo>
<msup>
<mi>&mu;D</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (10), λ presentation code Optimal Parameters;μ presentation code internal coordination parameters;
Step 6.6, note KlFor l-th of image classification c in N number of image classification ClThe sum of middle tag along sort;Using it is main into
Analysis method is divided to obtain the code optimization matrix of the t times iteration l subtasks studyMultiple characteristic values and with multiple features
It is worth corresponding characteristic vector, the multiple characteristic value is sorted from small to large, and selects preceding KlSpy corresponding to individual characteristic value
N-th of topological coded sub-matrices when sign vector is as the t times iteration
Step 6.7, judge whether l=N sets up, if so, n times tasking learning is then completed, so as to obtain opening up for the t+1 times iteration
Flutter encoder matrixAnd perform step 6.8;Otherwise l+1 value is assigned to l, repetition is held
Row step 6.4- steps 6.7;
Step 6.8, judge whether t=Γ set up, Γ is iteration total degree;If so, perform step 6.9;Otherwise by t+1 value
T is assigned to, return to step 6.2 sequentially performs;
Step 6.9, using F- normal forms to the t+1 times iteration topology encoder matrix W(t+1)Row vector calculated, obtain
Result of calculation be ranked up in descending order, select the t+1 times iteration topology encoder matrix W(t+1)Before middle sequence S row to
Amount, the preceding S row vector is obtained in the t+1 times iteration topology encoder matrix W(t+1)In corresponding line number { h1,
h2,…,hs,…,hS, hsRepresent that s-th of row vector is compiled in the t+1 times iteration topology in S row vector before the sequence
Code matrix W(t+1)In line number;1≤s≤S;
Step 6.10, from the topological structure { tp1′,tp2′,…,tp′ο,…,tp′σIn selection theIndividual topological structure,
So as to obtain the topological structure before the sequence in S row vector corresponding to s-th of row vector;So as to obtain S before the sequence
Topological structure corresponding to individual row vector, and form the topological dictionary Tp={ tp of the Aerial Images1″,tp2″,…,tp
″s′,…,tp″S′};tp″s′Represent the individual topological structures of s ' in the topological dictionary Tp;1≤s′≤S′;
Step 7, obtain the n-th image classification cnIn mnIndividual training Aerial ImagesFeature selecting vectorAnd
The target Aerial Images I0Feature selecting vector x0″;So as to obtain the n-th image classification cnIn mnIndividual training
Aerial ImagesThe tuple of feature selecting twoSo as to obtain the n-th image classification cnMiddle training is taken photo by plane
Image InThe tuple of feature selecting twoSo as to obtain
The training Aerial Images set I of the N number of image classification C tuple-set of feature selecting twoNote Represent the training Aerial Images set I of the N number of image classification C tuple-set X of feature selecting twoFSE-th
The tuple of feature selecting two, 1≤e≤E;
Step 7.1, definition selection number are ρ, and initialize ρ=1;1≤ρ≤S′;
Define the described the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix beAnd initialize described
The ρ mnIndividual training Aerial ImagesFeature Choice Matrix beFor sky;Define the ρ times mnIndividual training
Aerial ImagesFeature extraction matrix beAnd initialize
Step 7.2, choose the ρ topological structure tp " from topological dictionary Tpρ;
Step 7.3, judge the ρ topological structure tp "ρIn particle number whether be more than the mnIt is individual to train figure of taking photo by plane
PictureRegional distribution chartParticle number, if being more than, ρ+1 value is assigned to perform step 7.2 after ρ;Otherwise,
Perform step 7.4;
Step 7.4, by the ρ topological structure tp "ρWith the ρ times mnIndividual training Aerial ImagesEigenmatrixIn topological structure corresponding to each characteristic vector matched;If the match is successful, from the ρ times mnIndividual instruction
Practice Aerial ImagesFeature extraction matrixMiddle removal and the ρ topological structure tp "ρThe topology knot that the match is successful
Characteristic vector corresponding to structure, and it is put into the ρ times mnIndividual training Aerial ImagesFeature Choice Matrix be
In;
Step 7.5, ρ+1 value is assigned to ρ, judges whether ρ=S ' sets up, if so, using maximum pond strategy to the mnIt is individual
Train Aerial ImagesFeature Choice MatrixHandled, obtain the mnIndividual training Aerial ImagesFeature
Selection vectorOtherwise, step 7.2- steps 7.5 are repeated;
Step 8, the topological Boosting learning models of structure:
Step 8.1, utilize the individual topological structure tp " of s ' in the one-against-rest structure topological dictionary Tp of SVM classifiers′'s
Disaggregated model ψs′;So as to obtain the individual disaggregated model { ψ of S ' of the topological dictionary Tp1,ψ2,...,ψs′,...,ψS′};
Step 8.2, the iterations of definition Boosting learning models are τ, and initialize iterations τ=1;
DefinitionRepresent the training Aerial Images set I of the N number of image classification C during the τ times iteration tuple of feature selecting two
Set XFSThe tuple of e-th of feature selecting twoClass weight;Then N number of image classification C during the τ times iteration
Train the Aerial Images set I tuple-set X of feature selecting twoFSClass weight beAnd just
Beginningization
Step 8.3, using formula (11) obtain the τ times iteration optimization disaggregated model ψ(τ);
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<msup>
<mi>&psi;</mi>
<mrow>
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<mi>&tau;</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>=</mo>
<mi>arg</mi>
<mi> </mi>
<msub>
<mi>min</mi>
<mrow>
<msup>
<mi>&psi;</mi>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
</mrow>
</msup>
<mo>&Element;</mo>
<mo>{</mo>
<msub>
<mi>&psi;</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>&psi;</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>&psi;</mi>
<msup>
<mi>s</mi>
<mo>&prime;</mo>
</msup>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>&psi;</mi>
<msup>
<mi>S</mi>
<mo>&prime;</mo>
</msup>
</msub>
<mo>}</mo>
</mrow>
</msub>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>e</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>E</mi>
</msubsup>
<msubsup>
<mi>&omega;</mi>
<mi>e</mi>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>&times;</mo>
<mo>&lsqb;</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>p</mi>
<mi>&tau;</mi>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
<mi>S</mi>
</mrow>
</msubsup>
<mo>&RightArrow;</mo>
<msubsup>
<mi>c</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
<mi>S</mi>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (11),Represent using probability SVM methods to the disaggregated model ψ(τ)Handle the institute of acquisition
State e-th of tuple of feature selecting twoFeature selecting vectorBelong to image classificationProbability;
Step 8.4, utilize identification mistake err during formula (12) the τ times iteration of calculatingτ:
<mrow>
<msub>
<mi>err</mi>
<mi>&tau;</mi>
</msub>
<mo>=</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>e</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>E</mi>
</msubsup>
<msubsup>
<mi>&omega;</mi>
<mi>e</mi>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>&times;</mo>
<mo>&lsqb;</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>p</mi>
<mi>&tau;</mi>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
<mi>S</mi>
</mrow>
</msubsup>
<mo>&RightArrow;</mo>
<msubsup>
<mi>c</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
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</mrow>
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<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Step 8.5, utilize identification weight α during formula (13) the τ times iteration of calculatingτ:
<mrow>
<msub>
<mi>&alpha;</mi>
<mi>&tau;</mi>
</msub>
<mo>=</mo>
<mi>l</mi>
<mi>o</mi>
<mi>g</mi>
<mfrac>
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</msub>
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<mrow>
<msub>
<mi>err</mi>
<mi>&tau;</mi>
</msub>
</mrow>
</mfrac>
<mo>+</mo>
<mi>l</mi>
<mi>o</mi>
<mi>g</mi>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
Step 8.6, using formula (14) calculate the τ times iteration when described in e-th of class weightCorresponding classification transition weightSo as to obtain E classification transition weight
<mrow>
<msubsup>
<mi>&omega;</mi>
<mi>e</mi>
<mrow>
<mo>&prime;</mo>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
</mrow>
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<mo>=</mo>
<msubsup>
<mi>&omega;</mi>
<mi>e</mi>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>&times;</mo>
<mi>exp</mi>
<mo>{</mo>
<msub>
<mi>&alpha;</mi>
<mi>&tau;</mi>
</msub>
<mo>&times;</mo>
<mo>&lsqb;</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>p</mi>
<mi>&tau;</mi>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>X</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
<mi>S</mi>
</mrow>
</msubsup>
<mo>&RightArrow;</mo>
<msubsup>
<mi>c</mi>
<mi>e</mi>
<mrow>
<mi>F</mi>
<mi>S</mi>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>}</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>14</mn>
<mo>)</mo>
</mrow>
</mrow>
Step 8.7, to the E classification transition weightIt is normalized, obtains τ+1
Class weight during secondary iteration
Step 8.8, τ+1 value is assigned to τ, repeats step 8.3- steps 8.8;Untill τ is equal to iterations A, from
And obtain identification weight set { α1,α2,…,αa,…,αAAnd the A iteration optimization disaggregated model { ψ(1),ψ(2),…,
ψ(a),…,ψ(A)};αaRepresent a-th of identification weight in the identification weight set;ψ(a)Represent and described a-th identification weight
The disaggregated model of corresponding optimization;
Step 8.9, utilize the topological Boosting learning models acquisition target Aerial Images I shown in formula (15)0Image point
Class Δ;So as to realize the quick identification of target Aerial Images:
<mrow>
<mi>&Delta;</mi>
<mo>=</mo>
<mi>arg</mi>
<mi> </mi>
<msub>
<mi>min</mi>
<mrow>
<mi>&Delta;</mi>
<mo>&Element;</mo>
<mi>C</mi>
<mo>=</mo>
<mo>{</mo>
<msub>
<mi>c</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<msub>
<mi>c</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>c</mi>
<mi>n</mi>
</msub>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>c</mi>
<mi>N</mi>
</msub>
<mo>}</mo>
</mrow>
</msub>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>a</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>A</mi>
</msubsup>
<msub>
<mi>&alpha;</mi>
<mi>a</mi>
</msub>
<mo>&times;</mo>
<msub>
<mi>p</mi>
<mi>a</mi>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mn>0</mn>
<mrow>
<mo>&prime;</mo>
<mo>&prime;</mo>
</mrow>
</msubsup>
<mo>&RightArrow;</mo>
<mi>&Delta;</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (15), pa(x0" → Δ) represent the disaggregated model ψ to described a-th optimization using probability SVM methods(a)Located
Manage the target Aerial Images I obtained0Feature selecting vector x0" belong to the probability of image classification Δ.
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