CN107992897A - Commodity image sorting technique based on convolution Laplce's sparse coding - Google Patents
Commodity image sorting technique based on convolution Laplce's sparse coding Download PDFInfo
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- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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Abstract
The invention discloses a kind of commodity image sorting technique based on convolution Laplce's sparse coding, this method includes：Commodity image database is selected, obtains training image collection and test chart image set；The image that CLSC models concentrate training image is trained, learns, and convolution filter group and rarefaction representation feature are obtained from training sample；Convolution operation is carried out using the convolution filter group learnt and the image that test image is concentrated, the rarefaction representation feature of test image is obtained, be used to classify；Obtained training image rarefaction representation feature is trained support vector machines as training sample, and carries out parameter optimization；The rarefaction representation feature of test image is input in support vector machines, draws classification accuracy.Present invention reduces the redundancy of graphical representation, the classification accuracy of commodity image is effectively increased.
Description
Technical field
The present invention relates to a kind of commodity image sorting technique, belong to technical field of image processing, be specifically related to one
Commodity image sorting technique of the kind based on convolution Laplce's sparse coding.
Background technology
With the popularization and development of internet, the quantity of e-commerce website is in explosive growth, shopping website by
Gradually become the main source of people's daily shopping.In e-commerce initiative, that consumer and electric business operator face is not business
Product are in kind, but commodity image data and some simple marks, therefore image becomes the main information of merchandise news transmission
Carrier.How in the commodity image of magnanimity high-precision classification is put forward, so as to provide good user experience, being one is worth research
The problem of.
For now, the major technique of commodity image sorting technique is the extraction of characteristics of image and setting for grader
Meter.In terms of feature extraction, existing common method has sparse model.Sparse model learns training sample image, from instruction
Practice sample learning to sparse dictionary；Afterwards, sparse coding, the sparse coding are carried out to image to be sorted using sparse dictionary
As the rarefaction representation feature of image to be classified, it is used to classify.(such as sparse coding, Laplce is dilute for traditional sparse model
Dredge coding etc.) need to carry out piecemeal operation to image, realized by carrying out single sparse coding mode to each image block
The sparse bayesian learning of entire image, this method it is important there are one it is assumed that be between the image block of input it is mutually independent,
The continuity and correlation between adjacent image block are ignored, causes the high redundancy of coding.Although Laplce's sparse coding
(Laplacian Sparse Coding, LSC) adds a drawing on the basis of sparse coding (Sparse Coding, SC)
Pula this regularization term, it is contemplated that the uniformity between similar features, increases in image classification performance, but image
Redundancy is again without being well solved, so as to have impact on the accuracy of image classification.And the sparse volume of convolution Laplce
Code need not be described son using SIFT etc. and pre-define one group of image block to be learnt, and directly input picture can be grasped
Make, redundancy can be reduced and improve classification accuracy.
The content of the invention
The present invention is directed to issue noted above, it is proposed that one kind is based on convolution Laplce's sparse coding
The commodity image sorting technique of (Convolutional Laplacian Sparse Coding, CLSC).This method is directly to whole
Width image is operated, and graphical representation is converted to entire image and filtering by the one-dimensional inner product operation of image block and dictionary atom
The two-dimensional convolution operation of device, successfully solves the Redundancy of image, and improve the classification performance of commodity image.
To reach above-mentioned purpose, technical scheme and step are as follows：
S1：PI100 commodity images data set is simply pre-processed, and is classified as training image collection and test chart
Image set；
S2：The image concentrated using CLSC models to training image is trained, learnt, and convolution is obtained from training sample
Wave filter group；
S3：Convolution operation is carried out using the convolution filter group learnt and the image that test image is concentrated, is tested
The rarefaction representation feature of image, is used for image classification；
S4：The rarefaction representation feature that training image is obtained is as training sample to support vector machines (Support
Vector Machine, SVM) grader is trained, and carries out parameter optimization, reach the optimal performance of SVM；
S5：The rarefaction representation feature of test image is input to the SVM classifier after optimization, image classification is carried out and obtains
Classification accuracy.
In above-mentioned steps, data set PI100 is Microsoft Research from MSN shopping websites wherein used in step 1
The 10000 width commodity images collected, totally 100 classifications, are commodity image databases more common at present.Business in the database
Product image has the characteristics that resolution size is unified, background is single, as long as therefore carrying out simple pretreatment and can serve as inputting
Image is directly trained.
Above-mentioned steps 2 are key one step of the present invention in terms of feature extraction.This method is using CLSC algorithms to commodity figure
As carrying out feature learning, most important of which is that training study convolution filter group, by convolution filter group to test image
Rarefaction representation is carried out, obtains the feature representation of image.
Classification performance in above-mentioned steps 4 for non-linear SVM is vulnerable to the influences of the factors such as parameter, kernel function, and
With higher complexity during training.This method uses Linear SVM, and uses LBFGS algorithm optimization SVM, reaches SVM most optimal sortings
The parameter of class performance.
Compared with the commodity image sorting technique for being currently based on LSC, the invention has the advantages that：Calculated by CLSC
Method extraction image sparse represents feature, not only increases the discernment of feature, is also greatly reduced the redundancy of graphical representation, has
The classification accuracy of commodity image is improved to effect, there is preferable practicality.
Brief description of the drawings
Fig. 1 is sorting technique flow chart of the present invention
Fig. 2 is the commodity image schematic diagram used in present example
Fig. 3 is the visable representation for the convolution filter that present example learning arrives
Specific embodiment
In order to which technical scheme is more clearly understood, it is described in further detail below in conjunction with the accompanying drawings.
As shown in Figure 1, the commodity image sorting technique of the invention based on convolution Laplce's sparse coding, idiographic flow bag
Include following steps：
S1：Commodity image database is selected, the data set used in present example is the part figure in PI100 databases
Picture, totally 10 classifications, the picture number of each classification selection is the same.Using the 60% of data set as training image collection,
40% is used as test chart image set.
Data image is simply pre-processed, all coloured images are converted into gray level image, reach the work of dimensionality reduction
With, and data are normalized, improve the discernment of extraction feature.
S2：The image concentrated using CLSC models to training image is trained, learnt, and convolution is obtained from training sample
Wave filter group.
There is training datasetWherein xjRepresent j-th of training sample, size is m × m, nsRepresent that sample is total
Number.D=[D1,D2,…,Dc] represent convolution filter group, Di(1 ＜ i ＜ c) represents a two dimensional filter, and size is n × n.
The object function of CLSC models represents as follows：
Wherein, z=[z1,z2,…,zc] representing sparse features figure, c characteristic pattern forms image xj, ziSize be Represent convolution operation；Figure Laplce's matrix L is defined as L=H-W,It is one
A diagonal matrix；W=(ωij) it is a weight matrix, weights omegaijRepresent the similitude between image block.
Contain two variables in view of formula (1)：Convolution filter group D and sparse features figure z, this is that a combined optimization is asked
Topic, is non-convex problem, but keeps one of fixed, is then convex optimization problem when optimizing another.Therefore, this method is first solid
Determine wave filter group D, solve sparse features figure z；Then z is fixed, solves convolution filter group D.The two solution procedurees be all
Completed under multiplier alternating direction method (Alternating Direction Method of Multipliers, ADMM) frame,
And it is issued to convergence in the very fast time.
The image size used in present example is all 100 × 100, total sample number ns=1000, the wave filter of use
Size is 11 × 11, and the number of wave filter is 100.For CLSC algorithms, input is training datasetOutput
Be convolution filter group D, in the optimization process to formula (1), it is necessary first to D and z are initialized, utilize ADMM algorithms
Parameter renewal is carried out to it, until convergence (reach maximum iteration or target function value is less than the threshold value of setting).Pass through
CLSC Algorithm Learnings to wave filter group as shown in figure 3, the wave filter group effectively illustrates the direction of image, edge, profile, line
The architectural features such as reason, can effectively improve the discernment of feature.
S3：Convolution behaviour is carried out using the convolution filter group D that above-mentioned (2) learning arrives and the image that test image is concentrated
Make, obtained sparse features figure z*As the rarefaction representation feature of test image, be used to classify.
It is by the obtained optimal rarefaction representations of convolution filter group D：
S4：The rarefaction representation feature z that training image is obtained is as training sample to support vector machines (Support
Vector Machine, SVM) grader is trained, and carries out parameter optimization, reach the optimal performance of SVM；
In the present invention, using a kind of simple multiclass Linear SVM s.Given training data isThe target of one Linear SVM is study ncA linear functionFor example,
For a test data z, its class label can be by following formula predictions：
The present invention trains n using one-to-many strategycA binary linear SVMs, each needs to solve following nothing
Constrained convex optimal problem：
Wherein, if yc=c, thenOtherwise It is a loss function.The loss of standard
Function is non-differentiability everywhere, this is unfavorable for the application of the optimization method based on gradient.Here this method can be micro- using one
Quadratic loss function：
In this manner it is possible to relatively easily complete to train by the simply optimization method based on gradient.This method uses
Optimization method be LBFGS, select the optimized parameter of SVM, reach the optimal classification performance of SVM.
S5：By the rarefaction representation feature z of test image*It is input in linear more classification SVM after optimization, if more classification
The result of SVM outputs is consistent with test sample, shows that classification results are correct, correct sample number of classifying in statistical test image set
Divided by total sample number, obtain the accuracy rate classified for the commodity image of the test chart image set.
It is above-mentioned to describe in detail the commodity image classification side provided by the invention based on convolution Laplce's sparse coding
The applicating adn implementing of method, the present invention use CLSC algorithms, reduce the redundancy of graphical representation, while also demonstrate this method and exist
Superiority on commodity image classification accuracy.
Claims (2)
1. a kind of commodity image sorting technique based on convolution Laplce's sparse coding, this method comprises the following steps：
S1：PI100 commodity images data set is simply pre-processed, and is classified as training image collection and test image
Collection；
S2：The image concentrated using CLSC models to training image is trained, learnt, and convolutional filtering is obtained from training sample
Device group；
S3：Convolution operation is carried out using the convolution filter group learnt and the image that test image is concentrated, obtains test image
Rarefaction representation feature, be used for image classification；
S4：Using the rarefaction representation feature that training image obtains as training sample to support vector machines (Support Vector
Machine, SVM) grader is trained, and carries out parameter optimization, reach the optimal performance of SVM；
S5：The rarefaction representation feature of test image is input to the SVM classifier after optimization, image classification is carried out and is classified
Accuracy rate.
2. the commodity image sorting technique according to claims 1, it is characterised in that：
The image of training image collection is learnt using CLSC models, from training sample learning to convolution filter group, it
Afterwards, rarefaction representation is carried out to image to be sorted using convolution filter group, convolution filter group can be by optimizing following mesh
Scalar functions obtain：
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λ and β in above formula are parameter, and effect is openness and reconstitution for balancing, and is generally set during actual experiment
For constant (λ=0.1, β=0.1)；
Some are complicated, it is necessary to first fix one of them for the optimization process of above-mentioned object function, solve another, it is changed into one
A convex optimization problem：The solution procedure of D and z is all in multiplier alternating direction method (Alternating Direction Method
Of Multipliers, ADMM) complete under frame, by being continuously updated iteration, until algorithm reaches convergence, acquisition is optimal
Convolution filter group D and sparse features figure z；
The training sample of SVM classifier is the sparse features figure learnt from training image collection, and the present invention uses multiclass line
Property SVMs, by using a quadratic loss function that can be micro-：
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Answering for classifier training can be reduced by simply relatively easily completing to train based on the optimization method of gradient
Miscellaneous degree, this method optimize grader using LBFGS algorithms, reach the optimal classification performance of SVM；
Finally, image to be sorted is concentrated to carry out rarefaction representation test image by the convolution filter group D learnt, mainly
It is to carry out convolution operation using whole image to be classified and convolution filter, the step-length of convolution algorithm is 1, obtains test image
Sparse features figure, rarefaction representation feature of this sparse features figure as image to be classified, and it is entered into trained line
Property more classification SVM in；Classify in statistical test image set correct sample number divided by total sample number, obtain being directed to the test image
The accuracy rate of the commodity image classification of collection.
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