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Image classification method based on characteristic weight learning and nuclear sparse representation

A technology of kernel sparse representation and classification method, which is applied in the fields of instruments, character and pattern recognition, computer parts, etc.

Inactive Publication Date: 2013-02-13
XIDIAN UNIV
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Problems solved by technology

Although the KSRC method can improve the classification performance of the SRC method for samples in the same direction, neither the KSRC method nor the SRC method utilizes the category information of the training samples on the feature layer, so they do not have the image discrimination ability on the feature layer. Correctly classify images on the feature layer

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  • Image classification method based on characteristic weight learning and nuclear sparse representation
  • Image classification method based on characteristic weight learning and nuclear sparse representation
  • Image classification method based on characteristic weight learning and nuclear sparse representation

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specific Embodiment approach

[0037] refer to figure 1 , the specific embodiment of the present invention is as follows:

[0038] Step 1, input the training set and map it to the kernel subspace

[0039] First, input the training set X'={X' 1 ,X′ 2 ,...,X' C}, where X' p Represents the collection of the pth class samples in the training set, p=1,2,...,C, C represents the number of sample categories in the training set;

[0040] Secondly, use nonlinear kernel mapping, that is, the following Gaussian kernel function k(x, y), to map the samples of the training set X′ in the input sample space to the kernel space, and obtain the training set X″=[k(x i ,x j )] n×n ,

[0041] k(x,y)=exp(-t||x-y|| 2 ),

[0042] Wherein, x and y represent any two samples, ||·|| represents the distance between x and y, exp(·) is an exponential function, and t>0 is a parameter of the Gaussian kernel, which is set as the median value of is the mean of all training samples, x i and x j are the i-th and j-th training sa...

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Abstract

The invention discloses an image classification method based on characteristic weight learning and nuclear sparse representation and mainly solves the problem in the prior art that a characteristic layer judging capability is lacked. The image classification method comprises the realization steps of: mapping a training sample and a testing sample into a nuclear space; configuring a structuralized dictionary in the nuclear space; carrying out sparse representation on the training sample and the testing sample by utilizing the dictionary; solving a weight of each type of a sub-dictionary and a weight corresponding to a reconstructed error by utilizing a sparse coefficient of a training set through a Fisher judging principle; calculating the reconstructed error of the testing sample according to the dictionary weight and the weight of the reconstructed error; and selecting the minimum value from the reconstructed error of each type of the sub-dictionary to the testing sample, and taking the type of the corresponding sub-dictionary as a classifying result of the testing sample. According to the image classification method disclosed by the invention, the type judging capability of the dictionary and the reconstructed error on a characteristic layer can be enhanced; the performance of a classifier based on image reconstruction is improved; and the image classification method can be used for human face identification, image classification, image marking, image indexing and image division.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a classification method based on kernel sparse representation, which can be used to classify images. Background technique [0002] Image classification is one of the main topics in the field of computer vision and pattern recognition. In recent years, sparse representation methods have been successfully applied to image classification. In the classification SRC method based on sparse representation, the test sample is first represented by a linear combination of as few training samples as possible, and then the best linear representation of the sample is found by comparing the reconstruction errors of various types of data for the test sample. Class, classify the sample into this class. Yang et al. proposed an SRC-based FDDL model for pattern classification problems in the article: Fisher Discrimination Dictionary Learning for Sparse Representation. Suppose D=(D 1 ,D...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 郑喆坤简萌焦李成刘兵沈彦波刘娟
Owner XIDIAN UNIV
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