CN110097060A - A kind of opener recognition methods towards trunk image - Google Patents
A kind of opener recognition methods towards trunk image Download PDFInfo
- Publication number
- CN110097060A CN110097060A CN201910243209.3A CN201910243209A CN110097060A CN 110097060 A CN110097060 A CN 110097060A CN 201910243209 A CN201910243209 A CN 201910243209A CN 110097060 A CN110097060 A CN 110097060A
- Authority
- CN
- China
- Prior art keywords
- characteristic pattern
- similarity
- cluster
- trunk
- trunk image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of opener recognition methods towards trunk image, comprising the following steps: (1) CCN model is designed, after part training sample training CNN model, and construction feature extractor；(2) to the characteristic pattern extracted through feature extractor, the similarity between characteristic pattern is calculated, and characteristic pattern is clustered using DBSCAN algorithm；(3) Loss function is designed according to the cluster result of similarity and characteristic pattern between characteristic pattern, and according to the weight parameter of the parameter of the Loss function optimization feature extractor and similarity function；(4) trunk image to be sorted is input in the feature extractor of optimization, the similarity function of optimization is recycled to calculate the similarity between the characteristic pattern of output, the DBSCAN cluster result to characteristic pattern is finally obtained according to the similarity value for calculating acquisition.The opener recognition methods can realize the opener identification to the trunk image of unknown classification well.
Description
Technical field
The invention belongs to the deep learning algorithms of artificial intelligence field and opener identification research, and in particular to a kind of face
Opener recognition methods to trunk image.
Background technique
For a long time, opener recognition methods is always a research hotspot in deep learning field.This method be intended to from
Unknown sample is found in data and therefrom extracts useful feature information, is had in terms of signal identification and Face datection extensive
Using.Jain etc. proposes the opener recognition methods based on more classification SVM, they are using SVM to Probabilistic Decision-making score
It practises, and unknown sample is refused by setting probability threshold value.Bendale etc., which is proposed, carries out opener knowledge using deep neural network
Method for distinguishing, they introduce OpenMax layers, and estimate that input sample belongs to the probability of unknown class with this layer.Ge etc. is utilized
GAN generates unknown sample, reuses these data training neural network, is then combined it with OpenMax layers, while also using
One threshold value refuses unknown sample.Kuniaki etc. is that known class and unknown sample set up frontiers using dual training method,
And training generator makes target sample far from boundary.This method achieves on Office, VisDA and digits data set
Relatively good experimental result.
Similarity calculation is a kind of method for measuring similar or different degree between data.Two things are closer, it
Similarity measurement it is also bigger, and two things are more become estranged, their similarity measurement is also just smaller.Different similarities
Calculation method reflects the degree of closeness of data from different level.Common similarity algorithm include Euclidean distance, Manhattan away from
With a distance from, Minkowski, Pearson correlation coefficient, cosine similarity, Jaccard similarity factor etc., these calculation methods are used
The similarity degree data is described in the mode of quantification, so that other algorithms classify to these data.
Cluster is that the set of physics or abstract object is divided into the process that multiple classes are made of similar object.Cluster is usual
Be grouped according to the similitude between object, thus how the similitude between description object be cluster important prerequisite.K-means
Algorithm is a kind of clustering algorithm of classics, and the computation complexity of this method is lower, and on various data sets can obtain compared with
Good Clustering Effect.However, the cluster number K in algorithm generally requires artificially to give, which has limited K-means in actual scene
In application range.Density clustering method can automatically determine class cluster number, and DBSCAN algorithm is the typical case of such methods
It represents, it is defined as core point by introducing the reachable concept of density, the point that will be greater than a certain density, and adjacent core points are mutually straight
Connect reachable, all mutually reachable points form a class clusters, effectively compensate for the deficiency of K-means algorithm, and enhance to making an uproar
The processing capacity of sound point.
Convolutional neural networks are comprising convolutional calculation and with the feedforward neural network of depth structure, are the generations of deep learning
One of table algorithm.Convolutional neural networks copy the vision mechanism of biology to be constructed, and can exercise supervision study and non-supervisory
It practises, the sparsity that the convolution kernel parameter sharing in hidden layer is connected with interlayer enables convolutional neural networks with lesser calculating
It measures to extract the feature of image.As the proposition of deep learning theory and numerical value calculate the improvement of equipment, convolutional neural networks are obtained
Quick development has been arrived, and has been widely used in the fields such as computer vision, natural language processing.
Summary of the invention
The purpose of the present invention is designing a kind of opener recognition methods towards trunk image, pass through training designed, designed
CNN model obtains the feature extractor of image, determines loss function in conjunction with similarity calculating method and DBSCAN algorithm, passes through instruction
White silk further corrects the parameter of feature extractor, finally carries out feature extraction to the data of all categories on test set
And cluster, the opener of trunk image is identified to realize.
The technical solution of the present invention is as follows:
A kind of opener recognition methods towards trunk image, comprising the following steps:
(1) the CCN model towards trunk image is designed, after being trained using part training sample to CNN model, and root
According to trained CNN Construction of A Model feature extractor；
(2) to the characteristic pattern extracted through feature extractor, the similarity between characteristic pattern is calculated, and according to similarity value,
Characteristic pattern is clustered using DBSCAN algorithm；
(3) Loss function is designed according to the cluster result of similarity and characteristic pattern between characteristic pattern, and according to the Loss
Function and remaining training sample optimize the parameter of feature extractor and the weight parameter of similarity function；
(4) trunk image to be sorted is input in the feature extractor of optimization, is computed and obtains corresponding characteristic pattern,
The similarity function of recycling optimization calculates the similarity between characteristic pattern, finally according to the similarity value acquisition pair for calculating acquisition
The DBSCAN cluster result of characteristic pattern.
Opener recognition methods provided by the invention to trunk image can accurately extract inhomogeneity using feature extractor
Other otherness feature, and unknown class cluster can be captured by DBSCAN algorithm, it realizes and the opener of trunk data set is identified.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art, can be with root under the premise of not making the creative labor
Other accompanying drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart of the opener recognition methods provided by the invention towards trunk image；
Fig. 2 is the structural schematic diagram for the CNN model that the present invention constructs；
Fig. 3 is the real image in experimental example for 4 class trunks of training；
Fig. 4 is the real image in experimental example for the other 3 class trunk of opener identification.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, with reference to the accompanying drawings and embodiments to this
Invention is described in further detail.It should be appreciated that the specific embodiments described herein are only used to explain the present invention,
And the scope of protection of the present invention is not limited.
Present invention combination convolutional neural networks and density-based algorithms provide a kind of opening towards trunk image
Set identification method is trained feature extractor and similarity function using the trunk image of known class, utilizes training
Good feature extractor and similarity function, in conjunction with clustering method can the trunk image to unknown classification identify,
It realizes and the opener of trunk image is identified.
As shown in Figure 1, the opener recognition methods provided by the invention towards trunk image comprising the steps of:
S101 designs the CNN model towards trunk image.
It is repeatedly inquired into through testing, in order to promote the comprehensive and accuracy for extracting trunk characteristics of image, design is as shown in Figure 2
CNN model, specifically, CNN model includes sequentially connected input layer, the first convolutional layer, the first maximum pond layer, volume Two
Lamination, the second maximum pond layer, third convolutional layer, third maximum pond layer, Volume Four lamination, the first full articulamentum, second are entirely
Articulamentum and output layer, wherein the size of input layer is 256 × 256 × 3, that is, requires to input 3 Color Channels expressions, rulers
The very little trunk image for being 256 × 256；The template size of first convolution kernel is 11 × 11, port number 64, and convolution step-length is 4, warp
The size for crossing image after the first convolution operation becomes 64 × 64 × 64；The pond of the latter linked first maximum pond of first convolutional layer layer
Changing step-length is 2, and by the first Chi Huahou, the size of trunk image becomes 32 × 32 × 64；The template size of second convolution kernel is 5
× 5, port number 128, the picture size after the second convolution operation is 32 × 32 × 128；Is connected after second convolutional layer
The pond step-length of two maximum pond layers is 2, and by the second Chi Huahou, the size of trunk image becomes 16 × 16 × 128；Third volume
The template size of product core is 3 × 3, port number 192, and the picture size after third convolution operation is 16 × 16 × 192；The
The pond step-length of three convolutional layers latter linked third maximum pond layer is 2, and by third Chi Huahou, the size of trunk image becomes
8×8×192；The size of Volume Four product core is 3 × 3, port number 256, and the picture size after Volume Four product operation is 8
×8×256；The first full articulamentum is connected after Volume Four lamination, the image after first overall situation is average becomes 256 nerves
256 neurons are then mapped to 128 neurons by member, the full articulamentum of the second layer, finally by this 128 neurons and output layer
It is connected, through being exported as a result, the number of the neuron of output layer is corresponding with the classification number of classification after softmax.
S102 is trained CNN model using part training sample, and construction feature extractor.
After designing CNN model, i.e., CNN model is trained, specifically, using the tree comprising part trunk classification
The training sample of dry image and the practical category composition of corresponding trunk image is trained CNN model, loss when training
Function is the cross entropy for predicting category and practical category:
Wherein, p (i) indicates the practical category of i-th of trunk image, and q (i) indicates i-th of trunk image through CNN model meter
The prediction category of output is calculated, cross entropy L is smaller, then it represents that trunk image more connects with the characteristic pattern distribution to trunk image zooming-out
Closely, i.e. the prediction result of CNN model is more accurate.
After obtaining trained CNN model, remove the second full articulamentum in CNN model, remaining network structure with
And corresponding network architecture parameters constitute the feature extractor of trunk image.
S103 calculates the similarity between characteristic pattern to the characteristic pattern extracted through feature extractor, and according to similarity
Value, clusters characteristic pattern using DBSCAN algorithm.
After feature extractor extracts and obtains characteristic pattern, find to be to be between characteristic pattern there are certain similarity difference
From the similarity difference measured between different data with multi-angle, the present invention is linearly folded using cosine similarity and space similarity
The mode added obtains the similarity between two characteristic patterns, specifically, the calculation formula of similarity are as follows:
D(Ai,Aj)=- α [d (Ai,Aj)c+1]/2+βd(Ai,Aj)n (2)
Wherein, d (Ai,Aj)cIndicate ith feature figure AiWith j-th of characteristic pattern AjBetween cosine similarity, it use two
Angle between a vector describes the otherness between two characteristic patterns, specifically, d (Ai,Aj)cAre as follows:
It can be obtained from formula (3), d (Ai,Aj)cValue be [- 1,1], d (Ai,Aj)cValue it is smaller, then characteristic pattern AiAnd jth
A characteristic pattern AjSimilitude it is smaller.
For the ease of the use of DBSCAN algorithm later, to certain processing is carried out in formula (2), i.e., using-[d
(Ai,Aj)c+ 1] function D (A/2 is replacedi,Aj) in d (Ai,Aj)cIt is calculated, at this time-[d (Ai,Aj)c+ 1] value range/2
For [0,1], and-[d (Ai,Aj)c+ 1] value/2 is smaller, then characteristic pattern AiWith j-th of characteristic pattern AjSimilitude it is bigger, except this it
Outside, processing can guarantee D (A in this wayi,Aj) be positive always.
d(Ai,Aj)nIndicate ith feature figure AiWith j-th of characteristic pattern AjBetween Euclidean distance, as space phase knowledge and magnanimity
Value, for measuring absolute distance of two characteristic patterns in hyperspace, its calculation formula is:
Wherein, r indicates characteristic pattern AiWith characteristic pattern AjData dimension,WithRespectively indicate characteristic pattern AiAnd characteristic pattern
AjValue in kth dimension,Indicate characteristic pattern AiWith characteristic pattern AjIn kth dimension
Distance after normalization.It is easy to get from formula (4), d (Ai,Aj)nValue be [0,1], d (Ai,Aj)nValue it is smaller, then it is special
Sign figure AiWith characteristic pattern AjSimilitude it is bigger.
In formula (2), α and β are two weight coefficients, for adjusting Euclidean distance and cosine similarity in similarity meter
Proportion in calculation, and have α >=0, β >=0, alpha+beta=1 under initial situation, enable α=β=0.5, weigh during successive iterations
Weight parameter alpha and β will be obtained optimization.
After obtaining the similarity between characteristic pattern, according to similarity value, characteristic pattern is gathered using DBSCAN algorithm
Class, specifically, DBSCAN algorithm includes:
Define 1 (Eps neighborhood): for a characteristic pattern Ai, characteristic pattern AiEps neighborhood definition be with characteristic pattern AiFor core
The heart, using Eps as the hypersphere body region of radius, i.e.,
NEps(Ai)={ Aj∈M|D(Ai,Aj)≤Eps} (5)
Wherein, M indicates all feature set of graphs；
Define 2 (core point and boundary points): for a characteristic pattern Ai, an integer minPts is given, if characteristic pattern Ai's
Object in Eps neighborhood meets
|NEps(Ai)|≥min Pts (6)
Then claim characteristic pattern AiFor the core point under the conditions of (Eps, minPts)；
Define 3 (density is through): if characteristic pattern AiPositioned at characteristic pattern AjEps neighborhood in, and AjIt is kernel object, then claims
Characteristic pattern AiBy characteristic pattern AjDensity is through；
Define 4 (density is reachable): for characteristic pattern AiWith characteristic pattern Aj, sample sequence p if it exists1,p2,...,pt,
pt+1,...,pT, meet p1=Ai, pT=Aj, and pt+1By ptDensity is through, then claims AjBy AiDensity is reachable；It is tool that i.e. density is reachable
There is the density of transitivity through；
Work as Eps, after minPts is determined, for the characteristic pattern extracted through feature extractor, is made by any one characteristic pattern
It is starting for core point, a class cluster will be constituted with the reachable all characteristic patterns of the core dot density, when all core points quilt
When being divided to certain a kind of cluster, cluster is finished, and class cluster number will be determined automatically at this time.
S104 designs Loss function, and according to Loss function optimization feature extractor parameter.
When designing Loss function, according to the cluster result of S103, the core point for choosing all class clusters carries out Loss function
Calculating, and loss function is optimized using the optimization method based on gradient, specifically, Loss function are as follows:
Wherein, H indicates the set of all core points, g (Ai,Aj) it is sign function, as characteristic pattern AiWith characteristic pattern AjBelong to
When same class cluster, then g (Ai,Aj)=1；As characteristic pattern AiWith characteristic pattern AjWhen being not belonging to same class cluster, then g (Ai,Aj)=-
1.It follows that the purpose of L ' is to make the distance between same cluster core point small as far as possible, the distance between different cluster core points are to the greatest extent
Amount is big, so that training characteristics extractor is to improve Clustering Effect.
Using remaining training sample and the Loss function of determination, feature extractor is carried out using gradient descent algorithm
Parameter optimization, the feature extractor after optimization can reinforce the otherness feature extraction to each trunk classification.
S105 optimizes the weight parameter of similarity function.
When optimizing to feature extractor, when L ' tends towards stability, stop the parameter optimization to feature extractor, i.e.,
Obtain the feature extractor of optimization.In optimization feature extractor and then carry out similarity function D (Ai,Aj) in weight parameter α
With the optimization of β, due to α >=0, β >=0, alpha+beta=1, then similarity function D (Ai,Aj) write as:
D(Ai,Aj)=- α [d (Ai,Aj)c+1]/2+(1-α)d(Ai,Aj)n (8)
Firstly, Δ α=0.1 is now enabled, from initial value α0=0.5 starts, and calculates separately in α0Locate, in α0At+Δ α and α0-Δ
Cluster accuracy rate at α, corresponding α is as α when choosing cluster accuracy rate highest1；
Then, according to α1With α0Δ α is updated, and is calculated in α1Locate, in α1At+Δ α and α1Cluster accuracy rate at Δ α, choosing
Corresponding α is as α when taking cluster accuracy rate highest2, iteration process following procedure: i.e. according to αmWith αm-1Δ α is updated, and is counted
It calculates in αmLocate, in αmAt+Δ α and αmCluster accuracy rate at Δ α, corresponding α is as α when choosing cluster accuracy rate highestm+1,
Until finding optimal α, the similarity function D (A of optimal α, β=1- α and optimization is thus obtainedi,Aj)。
When calculating cluster preparation rate, cluster is calculated according to the corresponding practical category of characteristic pattern and acquisition cluster result
Accuracy rate.
S106 is computed and is corresponded in application, trunk image to be sorted is input in the feature extractor of optimization
Characteristic pattern, recycle optimization similarity function calculate characteristic pattern between similarity, finally according to calculate obtain it is similar
Angle value obtains the DBSCAN cluster result to characteristic pattern.
S107 exports cluster result.
Experimental example
In order to verify the classifying quality of the above-mentioned opener recognition methods towards trunk image, above-mentioned opener recognition methods is existed
The trunk of actual acquisition on collection according to being tested.
Trunk data set is the data set of team itself statistical collection, which includes 13 kinds of trunks, and every kind of trunk contains
There are about 200~500 trunk RPG images.In experiment, chooses preceding 7 class trunk and tested as test object.In order to make the number
Reach trainable requirement according to collection, data extending is carried out to such trunk image by intercepting part trunk at random, is then carried out
All trunk image processings are finally 256 × 256 sizes by gray processing processing.Finally obtain 500 picture of every kind of trunk, one
The data set of totally 3500 trunks.In experiment, before the data of preceding 4 kinds of trunks as shown in Figure 3 80% (totally 1600 figures are used
Piece) CNN model is trained as training set, and (i.e. as shown in Figure 4 by after 4 class trunks 20% data and other three classes
Classification trunk image) 20% data (totally 700 picture) detected as test set opener identification effect.Through counting,
Final cluster accuracy rate has reached 64.3%, and average cluster purity is 61.5%.The cluster accuracy rate of three classes opener trunk point
It Wei 58%, 54% and 49%.
The experimental example sufficiently shows the opener recognition methods provided by the invention towards trunk image can be real well
Now the opener of the trunk image of unknown classification is identified.
Technical solution of the present invention and beneficial effect is described in detail in above-described specific embodiment, Ying Li
Solution is not intended to restrict the invention the foregoing is merely presently most preferred embodiment of the invention, all in principle model of the invention
Interior done any modification, supplementary, and equivalent replacement etc. are enclosed, should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of opener recognition methods towards trunk image, comprising the following steps:
(1) the CCN model towards trunk image is designed, after being trained using part training sample to CNN model, and according to instruction
Experienced CNN Construction of A Model feature extractor；
(2) to the characteristic pattern extracted through feature extractor, the similarity between characteristic pattern is calculated, and according to similarity value, is used
DBSCAN algorithm clusters characteristic pattern；
(3) Loss function is designed according to the cluster result of similarity and characteristic pattern between characteristic pattern, and according to the Loss function
And remaining training sample optimizes the parameter of feature extractor and the weight parameter of similarity function；
(4) trunk image to be sorted is input in the feature extractor of optimization, is computed and obtains corresponding characteristic pattern, then benefit
The similarity between characteristic pattern is calculated with the similarity function of optimization, is finally obtained according to the similarity value for calculating acquisition to feature
The DBSCAN cluster result of figure.
2. as described in claim 1 towards the opener recognition methods of trunk image, which is characterized in that CNN model includes successively
The input layer of connection, the first convolutional layer, the first maximum pond layer, the second convolutional layer, the second maximum pond layer, third convolutional layer,
Third maximum pond layer, Volume Four lamination, the first full articulamentum, the second full articulamentum and output layer, wherein the ruler of input layer
Very little is 256 × 256 × 3, that is, require to input 3 Color Channels indicate, having a size of 256 × 256 trunk image；First convolution kernel
Template size be 11 × 11, port number 64, convolution step-length be 4, the size of image becomes 64 after the first convolution operation
×64×64；The pond step-length of the latter linked first maximum pond of first convolutional layer layer is 2, by the first Chi Huahou, trunk figure
The size of picture becomes 32 × 32 × 64；The template size of second convolution kernel is 5 × 5, and port number 128 is grasped by the second convolution
Picture size after work is 32 × 32 × 128；The pond step-length of the maximum pond layer of connection second is 2 after second convolutional layer, is passed through
The size of second Chi Huahou, trunk image become 16 × 16 × 128；The template size of third convolution kernel is 3 × 3, and port number is
192, the picture size after third convolution operation is 16 × 16 × 192；Third convolutional layer latter linked third maximum pond
The pond step-length of layer is 2, and by third Chi Huahou, the size of trunk image becomes 8 × 8 × 192；Volume Four product core size be
3 × 3, port number 256, the picture size after Volume Four product operation is 8 × 8 × 256；First is connected after Volume Four lamination
Full articulamentum, the image after first overall situation is average become 256 neurons, and the full articulamentum of the second layer is then by 256 nerves
Member maps to 128 neurons, and finally this 128 neurons are connected with output layer, through obtaining output knot after softmax
Fruit, the number of the neuron of output layer and the classification number of classification are corresponding.
3. as claimed in claim 2 towards the opener recognition methods of trunk image, which is characterized in that in step (1), using packet
The training sample of the trunk image of the classification of trunk containing part and the practical category composition of corresponding trunk image carries out CNN model
Training, loss function when training are the cross entropy for predicting category and practical category:
Wherein, p (i) indicates the practical category of i-th of trunk image, and it is defeated that q (i) indicates that i-th of trunk image is calculated through CNN model
Prediction category out；
After obtaining trained CNN model, the second full articulamentum in CNN model is removed, remaining network structure and right
The network architecture parameters answered constitute the feature extractor of trunk image.
4. as described in claim 1 towards the opener recognition methods of trunk image, which is characterized in that using cosine similarity and
The mode of space similarity linear superposition obtains the similarity between two characteristic patterns, specifically, the calculation formula of similarity
Are as follows:
D(Ai,Aj)=- α [d (Ai,Aj)c+1]/2+βd(Ai,Aj)n (2)
Wherein, d (Ai,Aj)cIndicate ith feature figure AiWith j-th of characteristic pattern AjBetween cosine similarity, d (Ai,Aj)nIt indicates
Ith feature figure AiWith j-th of characteristic pattern AjBetween Euclidean distance, α and β are two weight coefficients, for adjusting Euclidean distance
With proportion of the cosine similarity in similarity calculation, and there is α >=0, β >=0, alpha+beta=1.
5. as claimed in claim 4 towards the opener recognition methods of trunk image, which is characterized in that described to be calculated using DBSCAN
Method carries out cluster to characteristic pattern
Define 1 (Eps neighborhood): for a characteristic pattern Ai, characteristic pattern AiEps neighborhood definition be with characteristic pattern AiFor core, with
Eps is the hypersphere body region of radius, i.e.,
NEps(Ai)={ Aj∈M|D(Ai,Aj)≤Eps} (3)
Wherein, M indicates all feature set of graphs；
Define 2 (core point and boundary points): for a characteristic pattern Ai, an integer minPts is given, if characteristic pattern AiEps
Object in neighborhood meets
|NEps(Ai)|≥minPts (4)
Then claim characteristic pattern AiFor the core point under the conditions of (Eps, minPts)；
Define 3 (density is through): if characteristic pattern AiPositioned at characteristic pattern AjEps neighborhood in, and AjIt is kernel object, then claims feature
Scheme AiBy characteristic pattern AjDensity is through；
Define 4 (density is reachable): for characteristic pattern AiWith characteristic pattern Aj, sample sequence p if it exists1,p2,...,pt,pt+1,...,
pT, meet p1=Ai, pT=Aj, and pt+1By ptDensity is through, then claims AjBy AiDensity is reachable；It is with transitivity that i.e. density is reachable
Density it is through；
Work as Eps, after minPts is determined, for the characteristic pattern extracted through feature extractor, by any one characteristic pattern as core
Heart point is starting, a class cluster will be constituted with the reachable all characteristic patterns of the core dot density, when all core points are divided
When to certain a kind of cluster, cluster is finished, and class cluster number will be determined automatically at this time.
6. as claimed in claim 5 towards the opener recognition methods of trunk image, which is characterized in that use-[d (Ai,Aj)c+
1] function D (A/2 is replacedi,Aj) in d (Ai,Aj)cIt is calculated, to guarantee D (Ai,Aj) be positive always.
7. as claimed in claim 4 towards the opener recognition methods of trunk image, which is characterized in that Loss function are as follows:
Wherein, H indicates the set of all core points, g (Ai,Aj) it is sign function, as characteristic pattern AiWith characteristic pattern AjBelong to same
When a class cluster, then g (Ai,Aj)=1；As characteristic pattern AiWith characteristic pattern AjWhen being not belonging to same class cluster, then g (Ai,Aj)=- 1.
8. as claimed in claim 7 towards the opener recognition methods of trunk image, which is characterized in that utilize remaining trained sample
Sheet and the Loss function determined carry out parameter optimization to feature extractor using gradient descent algorithm.
9. as claimed in claim 8 towards the opener recognition methods of trunk image, which is characterized in that in optimization feature extractor
And then carry out similarity function D (Ai,Aj) in weight parameter α and β optimization, due to α >=0, β >=0, alpha+beta=1 is then similar
Spend function D (Ai,Aj) write as:
D(Ai,Aj)=- α [d (Ai,Aj)c+1]/2+(1-α)d(Ai,Aj)n (6)
Firstly, Δ α=0.1 is now enabled, from initial value α0=0.5 starts, and calculates separately in α0Locate, in α0At+Δ α and α0At Δ α
Accuracy rate is clustered, corresponding α is as α when choosing cluster accuracy rate highest1；
Then, according to α1With α0Δ α is updated, and is calculated in α1Locate, in α1At+Δ α and α1Cluster accuracy rate at Δ α is chosen poly-
Corresponding α is as α when class accuracy rate highest2, iteration process following procedure: i.e. according to αmWith αm-1Δ α is updated, and is calculated
αmLocate, in αmAt+Δ α and αmCluster accuracy rate at Δ α, corresponding α is as α when choosing cluster accuracy rate highestm+1, until
Until finding optimal α, the similarity function D (A of optimal α, β=1- α and optimization is thus obtainedi,Aj)。
10. as claimed in claim 9 towards the opener recognition methods of trunk image, which is characterized in that prepare calculating cluster
When rate, cluster accuracy rate is calculated according to the corresponding practical category of characteristic pattern and acquisition cluster result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910243209.3A CN110097060B (en) | 2019-03-28 | 2019-03-28 | Open set identification method for trunk image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910243209.3A CN110097060B (en) | 2019-03-28 | 2019-03-28 | Open set identification method for trunk image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110097060A true CN110097060A (en) | 2019-08-06 |
| CN110097060B CN110097060B (en) | 2021-04-06 |
Family
ID=67444070
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910243209.3A Active CN110097060B (en) | 2019-03-28 | 2019-03-28 | Open set identification method for trunk image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110097060B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110457511A (en) * | 2019-08-16 | 2019-11-15 | 成都数之联科技有限公司 | Image classification method and system based on attention mechanism and generation confrontation network |
| CN111179335A (en) * | 2019-12-28 | 2020-05-19 | 东北林业大学 | Standing tree measuring method based on binocular vision |
| CN111428760A (en) * | 2020-03-10 | 2020-07-17 | 上海集成电路研发中心有限公司 | Feature similarity based self-adaptive clustering method and application |
| CN111914919A (en) * | 2020-07-24 | 2020-11-10 | 天津大学 | Open set radiation source individual identification method based on deep learning |
| CN112508062A (en) * | 2020-11-20 | 2021-03-16 | 普联国际有限公司 | Open set data classification method, device, equipment and storage medium |
| CN113469276A (en) * | 2021-07-21 | 2021-10-01 | 苏州大学 | Fruit tree detection method and device |
| CN113822390A (en) * | 2021-11-24 | 2021-12-21 | 杭州贝嘟科技有限公司 | User portrait construction method and device, electronic equipment and storage medium |
| CN113947578A (en) * | 2021-10-18 | 2022-01-18 | 山东大学 | Prediction method of nucleation rate of vermicular cast iron based on DBSCAN clustering algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107729799A (en) * | 2017-06-13 | 2018-02-23 | 银江股份有限公司 | Crowd's abnormal behaviour vision-based detection and analyzing and alarming system based on depth convolutional neural networks |
| US9964607B1 (en) * | 2017-08-28 | 2018-05-08 | Seagate Technology Llc | Recognizing and identifying defect patterns on magnetic media |
| US10025950B1 (en) * | 2017-09-17 | 2018-07-17 | Everalbum, Inc | Systems and methods for image recognition |
| CN109214422A (en) * | 2018-08-02 | 2019-01-15 | 深圳先进技术研究院 | Parking data method for repairing and mending, device, equipment and storage medium based on DCGAN |
| US10235602B1 (en) * | 2018-03-08 | 2019-03-19 | Capital One Services, Llc | Machine learning artificial intelligence system for identifying vehicles |
-
2019
- 2019-03-28 CN CN201910243209.3A patent/CN110097060B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107729799A (en) * | 2017-06-13 | 2018-02-23 | 银江股份有限公司 | Crowd's abnormal behaviour vision-based detection and analyzing and alarming system based on depth convolutional neural networks |
| US9964607B1 (en) * | 2017-08-28 | 2018-05-08 | Seagate Technology Llc | Recognizing and identifying defect patterns on magnetic media |
| US10025950B1 (en) * | 2017-09-17 | 2018-07-17 | Everalbum, Inc | Systems and methods for image recognition |
| US10235602B1 (en) * | 2018-03-08 | 2019-03-19 | Capital One Services, Llc | Machine learning artificial intelligence system for identifying vehicles |
| CN109214422A (en) * | 2018-08-02 | 2019-01-15 | 深圳先进技术研究院 | Parking data method for repairing and mending, device, equipment and storage medium based on DCGAN |
Non-Patent Citations (7)
| Title |
|---|
| CHEN JINYIN等: ""A novel cluster center fast determination clustering algorithm"", 《APPLIED SOFT COMPUTING》 * |
| CHENBO FU等: ""Link weight prediction using supervised learning methods and its application to yelp layered network"", 《IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING》 * |
| JINYIN CHEN等: ""A novel image segmentation method based on fast density clustering algorithm"", 《ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE》 * |
| JINYIN CHEN等: ""FGCH: a fast and grid based clustering algorithm for hybrid data stream"", 《APPLIED INTELLIGENCE》 * |
| JUN HAN等: ""FlowNet: A deep learning framework for clustering and selection of streamlines and stream surfaces"", 《IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS》 * |
| 刘超等: ""超低照度下微光图像增强神经网络损失函数设计分析"", 《国防科技大学学报》 * |
| 李真等: ""树干与地面点云分类K-means方法的改进"", 《东北林业大学学报》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110457511A (en) * | 2019-08-16 | 2019-11-15 | 成都数之联科技有限公司 | Image classification method and system based on attention mechanism and generation confrontation network |
| CN110457511B (en) * | 2019-08-16 | 2022-12-06 | 成都数之联科技股份有限公司 | Image classification method and system based on attention mechanism and generation countermeasure network |
| CN111179335A (en) * | 2019-12-28 | 2020-05-19 | 东北林业大学 | Standing tree measuring method based on binocular vision |
| CN111428760A (en) * | 2020-03-10 | 2020-07-17 | 上海集成电路研发中心有限公司 | Feature similarity based self-adaptive clustering method and application |
| CN111428760B (en) * | 2020-03-10 | 2023-09-08 | 上海集成电路研发中心有限公司 | Self-adaptive clustering method based on feature similarity and application |
| CN111914919A (en) * | 2020-07-24 | 2020-11-10 | 天津大学 | Open set radiation source individual identification method based on deep learning |
| CN112508062A (en) * | 2020-11-20 | 2021-03-16 | 普联国际有限公司 | Open set data classification method, device, equipment and storage medium |
| CN113469276A (en) * | 2021-07-21 | 2021-10-01 | 苏州大学 | Fruit tree detection method and device |
| CN113947578A (en) * | 2021-10-18 | 2022-01-18 | 山东大学 | Prediction method of nucleation rate of vermicular cast iron based on DBSCAN clustering algorithm |
| CN113947578B (en) * | 2021-10-18 | 2024-08-09 | 山东大学 | Vermicular graphite cast iron nucleation rate prediction method based on DBSCAN clustering algorithm |
| CN113822390A (en) * | 2021-11-24 | 2021-12-21 | 杭州贝嘟科技有限公司 | User portrait construction method and device, electronic equipment and storage medium |
| CN113822390B (en) * | 2021-11-24 | 2022-03-15 | 杭州贝嘟科技有限公司 | User portrait construction method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110097060B (en) | 2021-04-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110097060A (en) | A kind of opener recognition methods towards trunk image | |
| CN112308158B (en) | Multi-source field self-adaptive model and method based on partial feature alignment | |
| CN106779087B (en) | A kind of general-purpose machinery learning data analysis platform | |
| CN110348399B (en) | Hyperspectral intelligent classification method based on prototype learning mechanism and multidimensional residual error network | |
| CN109034205A (en) | Image classification method based on the semi-supervised deep learning of direct-push | |
| CN114841257B (en) | Small sample target detection method based on self-supervision comparison constraint | |
| CN106960214A (en) | Object identification method based on image | |
| CN109543723B (en) | Robust image clustering method | |
| CN110807760B (en) | Tobacco leaf grading method and system | |
| CN110135459A (en) | A kind of zero sample classification method based on double triple depth measure learning networks | |
| CN110866134B (en) | Image retrieval-oriented distribution consistency keeping metric learning method | |
| CN102324038A (en) | A kind of floristics recognition methods based on digital picture | |
| CN106250925B (en) | A kind of zero Sample video classification method based on improved canonical correlation analysis | |
| CN110297888A (en) | A kind of domain classification method based on prefix trees and Recognition with Recurrent Neural Network | |
| CN110321451A (en) | Image retrieval algorithm based on Distribution Entropy gain loss function | |
| Huang et al. | MultiSpectralNet: Spectral clustering using deep neural network for multi-view data | |
| CN109993208A (en) | A kind of clustering processing method having noise image | |
| CN113628197A (en) | Weakly supervised full-section histopathology image classification method based on contrast learning | |
| CN110991554B (en) | Improved PCA (principal component analysis) -based deep network image classification method | |
| CN115393631A (en) | Hyperspectral image classification method based on Bayesian layer graph convolution neural network | |
| CN109948662A (en) | A kind of facial image depth clustering method based on K-means and MMD | |
| CN112149556B (en) | Face attribute identification method based on deep mutual learning and knowledge transfer | |
| CN109947960A (en) | The more attribute Combined estimator model building methods of face based on depth convolution | |
| CN113707213A (en) | Protein-ligand binding site prediction method based on deep learning | |
| CN116229330A (en) | Method, system, electronic equipment and storage medium for determining video effective frames |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |