Sparse image classification method based on low-rank supervision

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 coeffic...

Full description

Saved in:
Bibliographic Details
Main Authors LI AO, SUN GUANGLU, LEI TIANMING, LIN KEZHENG, CHEN DEYUN
Format Patent
LanguageChinese
English
Published 18.07.2017
Subjects
CALCULATING
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
Online AccessGet full text

Cover

More Information
Summary: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 solv
Bibliography:Application Number: CN201710209225