Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Image feature extraction method based on low-rank robust linear discriminant analysis

A linear discriminant analysis and image feature extraction technology, applied in the field of pattern recognition, can solve the problems of noise sensitivity and insufficient robustness, and achieve the effect of improving robustness, robustness and recognition performance

Active Publication Date: 2019-09-27
ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
View PDF4 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the purpose of the present invention is to propose an image feature extraction method based on low-rank robust linear discriminant analysis, to solve the problem that the prior art is sensitive to noise and not robust enough

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image feature extraction method based on low-rank robust linear discriminant analysis
  • Image feature extraction method based on low-rank robust linear discriminant analysis
  • Image feature extraction method based on low-rank robust linear discriminant analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0029] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0030] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are to distinguish two entities with the same name but different parameters or parameters that are not the same, see "first" and "second" It is only for the convenience of expression, and should not be construed as a limitation on the embodiments of the present invention, which will not be described one by one in the subsequent embodiments.

[0031] An image feature extraction method based on low-rank robust linear discriminant analysis, characterized in that, such as figure 1 As shown, the method includes the following steps:

[0032] 101 Use the robust principal component analysis algorithm based on low...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an image feature extraction method based on low-rank robust linear discriminant analysis. The method aims to overcome the defect that an LDA algorithm is sensitive to noise and robustness is bad. According to the method, a low-rank technology and an LDA algorithm are combined, an image feature extraction method for low-rank robust linear discriminant analysis is provided, and for a group of noisy data, the noise in the data can be separated while a low-rank representation method is used for learning a low-dimensional subspace structure of the data. Therefore, low-rank analysis is introduced into the LDA algorithm, so that the robustness of the algorithm can be improved, the algorithm is insensitive to noise, and the robustness and the recognition performance of the LDA algorithm are further improved.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to an image feature extraction method based on low-rank robust linear discriminant analysis. Background technique [0002] In application fields such as pattern recognition and machine learning, many image data are often encountered. Since image data are generally high-dimensional data, if the high-dimensional data is processed directly, the requirements for computer hardware are high, and the recognition rate is low. Therefore, before performing classification, recognition or clustering tasks on images, it is generally necessary to perform dimensionality reduction preprocessing on images, and feature extraction is one of the most common dimensionality reduction methods. [0003] At present, there have been many image feature extraction methods, such as principal component analysis (Principal Component Analysis, PCA), linear discriminant analysis (Linear Discriminant Analysis, LD...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/48G06F18/24147
Inventor 卢桂馥王勇唐肝翌许召辉
Owner ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com