CN109816025A - A kind of image search method based on image classification - Google Patents
A kind of image search method based on image classification Download PDFInfo
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- CN109816025A CN109816025A CN201910086700.XA CN201910086700A CN109816025A CN 109816025 A CN109816025 A CN 109816025A CN 201910086700 A CN201910086700 A CN 201910086700A CN 109816025 A CN109816025 A CN 109816025A
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Abstract
The invention discloses a kind of image search methods based on image classification, belong to Image Classfication Technology field, and the image search method includes the following steps: A: image preprocessing；B: image characteristics extraction；C: Selection of kernel function: selected RBF core is required kernel function, determine the parameter δ of RBF core and the value of penalty factor, a class value is taken to seek its classification function respectively, according to the predictablity rate of classification function and experience come adjusted value, the highest value of predictablity rate is found out as nuclear parameter, the parameter selection method based on grid data service needs the range of choice of prior given parameters, i.e. solution section, it is tested one by one in this section with certain step-length, finds the highest parameter of fitness and exported as algorithm；D: image classification；E: input test picture is into processing, to judge its belonging kinds, after test is errorless, can carry out picture retrieval.Not only algorithm is simple for search method of the present invention, and has preferable robustness.
Description
Technical field
The present invention relates to Image Classfication Technology field more particularly to a kind of image search methods based on image classification.
Background technique
The number letter such as rapid development of adjoint network and multimedia technology, including sound, figure, image, video and animation
Breath sharply expands.Image is concerned by people as a kind of abundant in content, intuitive media information of performance.In actual life
In at every moment there is a large amount of image to generate, the image for meeting user's requirement how is found out from these image informations, is to grind
The person's of studying carefully problem to be solved.Image classification is exactly the process of pattern-recognition, carries out quantitative analysis to image using computer,
In image each pixel or region incorporate into as one of several classifications, to replace the vision interpretation of people.The content of image
Rich and varied, the content abstraction for being included is complicated.Since the level that current image understanding and computer vision develop is limited,
There are larger differences for description of the people to the understanding and computer of image to image.And different people is to the reason of same piece image
There is also gap or even far from each other, such problems for solution and description, are all the difficulties that image classification needs to consider and solve
Topic.In order to quickly tell the classification of image, needs to carry out image classification first, then carry out image retrieval.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, it the present invention provides a kind of image search method based on image classification, solves
The problem of existing search method inconvenience.
(2) technical solution
To achieve the above object, the invention provides the following technical scheme: a kind of image retrieval side based on image classification
Method, image search method include the following steps:
A: it image preprocessing: downloads different pictures and is input in processor by scanner, a part of picture is used as
Training picture, the picture of another part is as test picture；
B: image characteristics extraction: selection LIBSVM software carries out image classification, to the color and textural characteristics of training picture
It extracts, and using color characteristic and textural characteristics as the classification of LIBSVM software；
C: Selection of kernel function: selected RBF core is required kernel function, determines the parameter δ and penalty factor of RBF core
Value, takes a class value to seek its classification function respectively, according to the predictablity rate of classification function and experience come adjusted value, finds out prediction
The highest value of accuracy rate is used as nuclear parameter, and the parameter selection method based on grid data service needs the selection of prior given parameters
Range, i.e. solution section, are tested one by one with certain step-length in this section, it is defeated as algorithm to find the highest parameter of fitness
Out；
D: N (N-1)/2 SVM two classification device, the instruction of each classifier image classification: are constructed for N class classification problem
Practicing sample is relevant two classes, combines these two classification devices and classification number is a classification Support matrix, and use mould
Plate similarity mode method, the highest class of similarity are classification belonging to sample；
E: input test picture is into processing, to judge its belonging kinds, after test is errorless, can carry out picture retrieval.
Preferably, a kind of image search method based on image classification according to claim 1, it is characterised in that:
In step C, the calculation of the kernel function of the RBF core is as follows:Wherein δ is RBF
The parameter of kernel function, x represent different data, x-xiRepresent data subspace.
Preferably, in step C, the method that grid data service finds optimal nuclear parameter includes the following steps: selected C and δ
Range, choose C ∈ (2-5, 2-3... 215), l/ δ2∈(2-15,2-13..., 23), progress coarse grid search first, setting is searched
Suo Buchang is 1, in this way, constituting a two-dimensional grid, each group of C on corresponding grid on δ coordinate system in C, δ value is all one group latent
It is solving, is representing one group of SVM parameter, the mean value of each group parameter prediction accuracy rate is calculated according to K folding cross validation method, use is contour
Line is drawn, and a contour map is obtained, and determines optimal C, δ parameter pair.
Preferably, optimal C is determined, δ parameter is to rear, then has carried out carrying out a refined net again after coarse grid search searching
Rope selectes a region of search that is, on existing contour map, selectes the highest region of predictablity rate, reduces search step
It is long to carry out binary search.
Preferably, reducing search section step-length is 0.1.
Preferably, in step D, it is equipped with R classifier D={ D0, D1... DR-1), problem to be processed has C classification,
If input sample X, the output of classifier mouth is C dimensional vector: Di(x)=[di,0(x),di,1(x),…di,c-1(x)],
In, wherein di,j(x) (j=0,1 ..., C-1) presentation class device diThe support for being class j to sample X judgement, by all classification
The output result of device builds up classification Support matrix.
Preferably, in step D, template similarity matching method refers to classification Support matrix to be sorted and owns
The decision template of classification compares, most like classification, that is, current sample classification.
(3) beneficial effect
The present invention provides a kind of image search methods based on image classification, have following the utility model has the advantages that of the invention
Statistical method using SVM classifier as image classification, therefore it is different from existing statistical method.It has the following advantages:
The terminal decision function of SVM classifier is only determined that the complexity of calculating depends on supporting vector by a small number of supporting vectors
Number, rather than the dimension of sample space, this avoids " dimension disaster " in some sense.A small number of supporting vectors determine
Final result, this can not only help we grasp the key link sample, reject bulk redundancy sample, and be doomed this method not
But algorithm is simple, and has preferable robustness.Guarantee due to there is more stringent Statistical Learning Theory to do, using SVM
The model that classifier is established has preferable Generalization Ability.SVM classifier can provide the determination of the Generalization Ability of model built
The upper bound, this is not available for current other any learning methods.Any one data model is established, artificial intervention is got over
It is few more objective.It is compared with other methods, it is less to establish the intervention of priori required for SVM model.
Specific embodiment
Below in conjunction with the embodiment of the present invention, technical solution in the embodiment of the present invention is clearly and completely retouched
It states, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
A kind of image search method based on image classification, image search method include the following steps:
A: it image preprocessing: downloads different pictures and is input in processor by scanner, a part of picture is used as
Training picture, the picture of another part is as test picture；
B: image characteristics extraction: selection LIBSVM software carries out image classification, to the color and textural characteristics of training picture
It extracts, and using color characteristic and textural characteristics as the classification of LIBSVM software；
C: Selection of kernel function: selected RBF core is required kernel function, determines the parameter δ and penalty factor of RBF core
Value, the calculation of the kernel function of the RBF core are as follows:Wherein δ is RBF kernel function
Parameter, x represent different data, x-xiData subspace is represented, takes a class value to seek its classification function respectively, according to classification function
Predictablity rate and experience carry out adjusted value, find out the highest value of predictablity rate as nuclear parameter, based on grid data service
Parameter selection method needs the range of choice of prior given parameters, i.e. solution section, is tried one by one in this section with certain step-length
It tests, finds the highest parameter of fitness and exported as algorithm；The method that grid data service finds optimal nuclear parameter includes following step
Rapid: the range of selected C and δ chooses C ∈ (2-5, 2-3... 215), l/ δ2∈(2-15,2-13..., 23), coarse grid is carried out first
Search, set step-size in search is 1, in this way, in C, one two-dimensional grid of composition on δ coordinate system, and each group of C, δ on correspondence grid
Value is all one group of potential solution, represents one group of SVM parameter, calculates each group parameter prediction accuracy rate according to K folding cross validation method
Mean value, drawn with contour, obtain a contour map, determine optimal C, δ parameter pair；Determine optimal C, δ parameter pair
Afterwards, then after having carried out coarse grid search a refined net search is carried out again, i.e., a search is selected on existing contour map
The highest region of predictablity rate is selected in region, is reduced step-size in search and is carried out binary search, and reducing step-size in search is 0.1.
D: N (N-1)/2 SVM two classification device, the instruction of each classifier image classification: are constructed for N class classification problem
Practicing sample is relevant two classes, combines these two classification devices and classification number is a classification Support matrix, be equipped with R
Classifier D={ D0, D1... DR-1), problem to be processed has C classification, if input sample X, the output of classifier mouth is one
C dimensional vector: Di(x)=[di,0(x),di,1(x),…di,c-1(x)], wherein wherein di,j(x) (j=0,1 ..., C-1) table
Show classifier diThe support for being class j to sample X judgement, builds up classification Support matrix for the output result of all classifiers,
And template similarity matching method is used, the highest class of similarity is classification belonging to sample, and template similarity matching method refers to
The decision template of classification Support matrix and all categories to be sorted is compared, most like classification, that is, current sample
Classification；
E: input test picture is into processing, to judge its belonging kinds, after test is errorless, can carry out picture retrieval.
The present invention uses statistical method of the SVM classifier as image classification, therefore is different from existing statistical method.
It has the following advantages: the terminal decision function of SVM classifier is only determined by a small number of supporting vectors, the complexity of calculating
Depending on the number of supporting vector, rather than the dimension of sample space, this avoids " dimension disaster " in some sense.It is few
Number supporting vectors determine final result, this can not only help we grasp the key link sample, reject bulk redundancy sample, and
And this method has been doomed it not only algorithm is simple, and there is preferable robustness.Due to there is more stringent Statistical Learning Theory
It does and guarantees, there is preferable Generalization Ability using the model that SVM classifier is established.SVM classifier can provide model built
Generalization Ability determination the upper bound, this is not available for current other any learning methods.Establish any one data mould
The fewer type, artificial intervention the more objective.It is compared with other methods, it is less to establish the intervention of priori required for SVM model.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation
There are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant are intended to
Cover non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", not
There is also other identical elements in the process, method, article or apparatus that includes the element for exclusion.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (7)
1. a kind of image search method based on image classification, it is characterised in that: image search method includes the following steps:
A: image preprocessing: downloading different pictures and be input in processor by scanner, and a part of picture is used as training
Picture, the picture of another part is as test picture；
B: image characteristics extraction: selection LIBSVM software carries out image classification, carries out to the color and textural characteristics of training picture
It extracts, and using color characteristic and textural characteristics as the classification of LIBSVM software；
C: Selection of kernel function: selected RBF core is required kernel function, determines the parameter δ of RBF core and the value of penalty factor, takes
One class value seeks its classification function respectively, according to the predictablity rate of classification function and experience come adjusted value, finds out predictablity rate
Highest value is used as nuclear parameter, and the parameter selection method based on grid data service needs the range of choice of prior given parameters, i.e.,
Section is solved, is tested one by one in this section with certain step-length, the highest parameter of fitness is found and is exported as algorithm；
D: N (N-1)/2 SVM two classification device, the training sample of each classifier image classification: are constructed for N class classification problem
Originally it is relevant two classes, combines these two classification devices and classification number is a classification Support matrix, and use template phase
Like degree matching method, the highest class of similarity is classification belonging to sample；
E: input test picture is into processing, to judge its belonging kinds, after test is errorless, can carry out picture retrieval.
2. a kind of image search method based on image classification according to claim 1, it is characterised in that: in step C,
The calculation of the kernel function of the RBF core is as follows:Wherein δ is the ginseng of RBF kernel function
Number, x represent different data, x-xiRepresent data subspace.
3. a kind of image search method based on image classification according to claim 2, it is characterised in that: in step C,
The method that grid data service finds optimal nuclear parameter includes the following steps: the range of selected C and δ, chooses C ∈ (2-5, 2-3,
...215), l/ δ2∈(2-15,2-13..., 23), progress coarse grid search first set step-size in search as 1, in this way, in C, δ seat
Mark, which is fastened, constitutes a two-dimensional grid, and each group of C on corresponding grid, δ value is all one group of potential solution, represents one group of SVM parameter, presses
The mean value that each group parameter prediction accuracy rate is calculated according to K folding cross validation method, is drawn with contour, obtains a contour map,
Determine optimal C, δ parameter pair.
4. a kind of image search method based on image classification according to claim 3, it is characterised in that: determine optimal
C, δ parameter carry out a refined net search to rear, then after having carried out coarse grid search again, i.e., select on existing contour map
The highest region of predictablity rate is selected in one region of search, reduces step-size in search and carries out binary search.
5. a kind of image search method based on image classification according to claim 4, it is characterised in that: reduce search section
Step-length is 0.1.
6. a kind of image search method based on image classification according to claim 1, it is characterised in that: in step D,
Equipped with R classifier D={ D0, D1... DR-1) (right parenthesis should be }), problem to be processed has C classification, if input sample
X, the output of classifier mouth are C dimensional vectors: Di(x)=[di,0(x),di,1(x),…di,c-1(x)], wherein wherein di,j
(x) (j=0,1 ..., C-1) presentation class device diThe support for being class j to sample X judgement, by the output knot of all classifiers
Fruit builds up classification Support matrix.
7. a kind of image search method based on image classification according to claim 1, it is characterised in that: in step D,
Template similarity matching method refers to that the decision template by classification Support matrix and all categories to be sorted compares, most phase
As classification, that is, current sample classification.
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| US8611617B1 (en) * | 2010-08-09 | 2013-12-17 | Google Inc. | Similar image selection |
| CN102402621A (en) * | 2011-12-27 | 2012-04-04 | 浙江大学 | Image retrieval method based on image classification |
| CN104809472A (en) * | 2015-05-04 | 2015-07-29 | 哈尔滨理工大学 | SVM-based food classifying and recognizing method |
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