Sparse image classification method based on low-rank supervision

A sparse image and classification method technology, applied in the field of image processing, can solve the problems of not making full use of image sparse representation and low-rank features, poor versatility, etc., and achieve the effect of good discrimination and good classification effect

Inactive Publication Date: 2017-07-18
HARBIN UNIV OF SCI & TECH
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0004] In order to solve the problem that the existing image classification method has poor versatility and does not make full use of the sparse representation and low-rank features of the image, the present invention discloses a sparse image

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  • Sparse image classification method based on low-rank supervision
  • Sparse image classification method based on low-rank supervision
  • Sparse image classification method based on low-rank supervision

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Example Embodiment

[0055] Specific embodiment one

[0056] The sparse image classification method based on low-rank supervision of this embodiment, the flowchart is as follows figure 1 Shown. The classification method includes the following steps:

[0057] Step a: Determine the known sample and the sample to be classified;

[0058] Step b: Calculate the low-rank representation coefficient matrix Z of the sample to be classified;

[0059] Step c: Solve the supervision matrix W according to the low-rank representation coefficient matrix Z;

[0060] Step d: Establish a sparse coding model based on the supervision matrix;

[0061] Step e: Iteratively solve the sparse coding model;

[0062] Step f, classify according to reconstruction error;

[0063] Step g, calculate and analyze the classification accuracy rate Rate.

Example Embodiment

[0064] Specific embodiment two

[0065] In the sparse image classification method based on low-rank supervision in this embodiment, on the basis of specific embodiment 1, the specific method for further limiting step a is:

[0066] Step a1: Input the sample data in the image library, and normalize it;

[0067] Step a2. Mark the number of classes of sample data and the number of samples in each class respectively, and divide each class of samples into a known sample matrix X={X 1 , X 2 , X 3 ,..., X k } And the sample matrix to be classified Y = {y 1 , Y 2 , Y 3 ,..., y n },among them, Represents the m-th sample vector in the k-th sample, y n Represents the n-th sample vector to be classified; both the sample vector and the sample vector to be classified are vectors obtained by transforming the image matrix in the order of rows.

Example Embodiment

[0068] Specific embodiment three

[0069] The sparse image classification method based on low-rank supervision in this embodiment, on the basis of specific embodiment 1, further restricts the method of calculating the low-rank representation coefficient matrix Z of the sample to be classified in step b as follows:

[0070] min||Z|| * +λ||E|| 1 s.t.Y=YZ+E (1)

[0071] Among them, E represents the residual matrix, the elements in the matrix Z represent the correlation between samples, λ is the regularization parameter, ||·|| * =∑ i |σ i (Z)|,σ i (·) means to take the i-th singular value of the matrix, s.t.Y=YZ+E means min||Z||*+λ||E|| 1 Limited by Y=YZ+E, the solution of formula (1) adopts the augmented Lagrangian multiplier method.

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Abstract

The invention belongs to the image processing technology field and especially relates to a sparse image classification method based on low-rank supervision. The method comprises the following steps of determining a known sample and a sample to be classified; calculating a low-rank expression coefficient matrix Z of the sample to be classified; according to the low-rank expression coefficient matrix Z, solving a supervision matrix W; establishing a sparse coding model based on the supervision matrix; carrying out iteration solving on the sparse coding model; according to a reconstruction error, carrying out classification; and calculating and analyzing a classification correct rate Rate. In the invention, through establishing the sparse coding model based on low-rank supervision and providing a solving method of the sparse coding model based on low-rank supervision, simultaneously sparsity of an expression coefficient and an approaching degree of similar samples are restrained; a weight W is expressed and solved by a low rank so that a sample characteristic can be well captured; and then the model possesses a good discriminating ability among samples so that a good classification effect is acquired.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a sparse image classification method based on low-rank supervision. Background technique [0002] With the development of information technology, image information is widely used in various fields to obtain a large amount of content contained in it, which makes people have a lot of demand for image processing technology. As an important part of image processing, image classification has been widely used in artificial intelligence, computer vision, pattern recognition, information search and other technical fields in recent years. Image classification is to classify images according to the information, content, and features in the images, so as to simulate human's visual understanding and logical interpretation of images, so as to achieve the purpose of intelligence. At present, many research departments and commercial companies have begun or are studying ima...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 李骜雷天鸣陈德运林克正孙广路
Owner HARBIN UNIV OF SCI & TECH
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