Image data dimension reduction method and system based on discriminant regularization local reservation projection

A technology that locally preserves projection and image data, applied in the field of image recognition, can solve problems such as insufficient consideration of differences in distribution characteristics, avoid distortion, prevent the introduction of too much non-local information, and improve local essential structural features.

Active Publication Date: 2020-08-18
XIAMEN UNIV
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Problems solved by technology

However, the scale of the sparse representation has nothing to do, making these methods insufficiently consider the differences in the distribution characteristics of different regions

Method used

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  • Image data dimension reduction method and system based on discriminant regularization local reservation projection
  • Image data dimension reduction method and system based on discriminant regularization local reservation projection
  • Image data dimension reduction method and system based on discriminant regularization local reservation projection

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Embodiment approach

[0068] As an implementation manner, step 108 specifically includes:

[0069] Obtain the image to be reduced in dimension;

[0070] Cutting the image to be dimensionally reduced into a sample vector x;

[0071] According to Y=W 'T x performs dimensionality reduction on the image to be reduced in dimensionality, wherein W' is a target projection matrix, and Y is data after dimensionality reduction of the image to be reduced in dimensionality.

[0072] As an implementation manner, the method provided in this embodiment also includes:

[0073] Get the test sample image;

[0074] Cutting the test sample image into test sample vectors;

[0075] performing dimensionality reduction on the test sample vector by using the target projection matrix;

[0076] A classifier is used to identify the test sample vector after dimensionality reduction;

[0077] Evaluate the pros and cons of the projection matrix according to the recognition result.

[0078] In this embodiment, the dimensio...

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Abstract

The invention discloses an image data dimension reduction method and system based on discriminant regularization locality preserving projection, wherein the method comprises the following steps: acquiring a sample image; cutting the sample image into sample vectors, and performing L2 norm normalization processing on each sample vector to obtain a processed sample vector xi; determining a first similarity matrix Sij; according to the determined second similarity matrix S'ij, W is a projection matrix, and Bij is an elastic matrix; solving a matrix V and a characteristic value lambda according to(X (L + lambda L ') XT) V = lambda (XDXT) V; extracting feature vectors corresponding to the relatively large first v feature values in the matrix V to form a projection matrix; skipping to a step ofdetermining a second similar matrix until an iteration condition is met, and recording a finally obtained projection matrix as a target projection matrix; and performing dimension reduction processing on the image to be subjected to dimension reduction by adopting the target projection matrix. According to the image data dimension reduction method and system provided by the invention, the similarity and difference of the data are considered at the same time.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image data dimensionality reduction method and system based on discriminant regularization local preservation projection. Background technique [0002] Data dimensionality reduction of images is to project high-dimensional data into low-dimensional space while maintaining as much intrinsic information as possible of the original data, so that high-dimensional data can be represented in low-dimensional space. Through this operation, the redundancy of the original data can be reduced, and the efficiency and pertinence of data processing can be improved. The most typical dimensionality reduction methods for linear data dimensionality reduction include: Principal component analysis (PCA) and linear discriminant analysis (Linear discriminant analysis, LDA). These two methods are mature in theory, simple in calculation and fast in calculation speed, but these methods are ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/213
Inventor 高云龙潘金艳陈福兴
Owner XIAMEN UNIV
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