Multi-Dimensional Color Image Recognition and Mining Based on Feature Mining Algorithm

This paper introduced two algorithms of image feature mining: Histogram of Oriented Gradient (HOG) and gray-level co-occurrence matrix (GLCM). Then, the two algorithms were combined with support vector machine (SVM) model respectively to identify and classify color image. Simulation experiment was c...

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Bibliographic Details
Published inAutomatic control and computer sciences Vol. 55; no. 2; pp. 195 - 201
Main Authors Chen, Jiming, Chen, Liping
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.03.2021
Springer Nature B.V
Subjects
Algorithms
Color imagery
Computer Science
Control Structures and Microprogramming
Histograms
Image classification
Mathematical analysis
Matrix methods
Object recognition
Support vector machines
color image
histogram of oriented gradient
gray-level co-occurrence matrix
feature mining
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ISSN0146-4116
1558-108X
DOI10.3103/S0146411621020048

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Summary:This paper introduced two algorithms of image feature mining: Histogram of Oriented Gradient (HOG) and gray-level co-occurrence matrix (GLCM). Then, the two algorithms were combined with support vector machine (SVM) model respectively to identify and classify color image. Simulation experiment was carried out in MATLAB R2018a software. The results showed that the feature texture image obtained by GLCM was more clear and accurate than that obtained by HOG; the GLCM based SVM had higher and more stable accuracy and shorter and more stable detection time.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411621020048