Image classification method based on low-rank sparse representation

A technology of sparse representation and classification method, applied in the field of image recognition, it can solve the problem of not being able to process new samples, and achieve the effect of strong robustness

Active Publication Date: 2018-05-11
HENAN UNIV OF SCI & TECH
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an image classification method ba

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  • Image classification method based on low-rank sparse representation
  • Image classification method based on low-rank sparse representation
  • Image classification method based on low-rank sparse representation

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[0043]An image classification method based on low-rank sparse representation, including the following steps:

[0044] Step 1, from the C class training sample matrix A=[A 1 ,A 2 ,...,A c ]∈R m×N , test sample Y=[y 1 ,y 2 ,...,y M ]∈R m×M , normalize each column of the training sample A and the test sample Y to the unit L 2 Norm, where N represents the number of training samples, M represents the number of test samples, and m represents the dimension of the sample.

[0045] Step 2. Calculate the projection matrix P:

[0046] Step 2.1, initialization: make parameters λ>0, γ>0, α>0, η>0, Z 0 =W 0 =E 0 =Y 10 =Y 20 =0,P 0 is a random matrix, μ 0 =0.1,μ max =10 10 , ρ=1.1, ε=10 -3 , maxiter=1000, k=0, where μ is the penalty parameter, k is the number of iterations, η and α are balance factors, Z, W and E are coefficient matrices, Y 1k , Y 2k is the Lagrangian multiplier;

[0047] Step 2.2, use the formula (1) to update the coefficient matrix Z:

[0048]

[0...

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Abstract

The invention discloses an image classification method based on low-rank sparse representation, and the method comprises the following steps: carrying out the normalization of each column of a training sample A and a test sample Y into a unit L2 norm; calculating a projection matrix P; calculating a projection matrix A' of the training sample A; calculating a projection matrix Y' of the test sample Y; and completing a classification task through a nearest neighbor classifier. The beneficial effects of the invention are that the method enables the sparse representation, low-rank representationand discrimination projection to be integrated in one frame, and gives consideration to the local and global structure information of the observation data; compared with other dimension-reduction methods, the method is highly robust to outlier points and noise.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image classification method based on low-rank sparse representation. Background technique [0002] Image recognition is one of the most attractive and challenging research topics in biometrics, computer vision and machine learning. However, the original data images are usually high-dimensional, which will result in a large amount of computation and high memory requirements in the image recognition process. Moreover, the original high-dimensional image data usually contains a lot of noise information, which will degrade the performance of image recognition. To address these issues, many feature extraction methods have been proposed for dimensionality reduction. The most classic and representative dimensionality reduction methods are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is an unsupervised algorithm, in which the global varia...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2136G06F18/241
Inventor 刘中华张琳陈永刚刘刚郑林涛普杰信
Owner HENAN UNIV OF SCI & TECH
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