Texture information fusion based image classification

The classical gray level co-occurrence matrix(GLCM) neglects the directional differences of texture. A novel method of image classification is presented in this paper. Multi-angle weighting of GLCM is proposed to decrease effects of directional difference, in which second-order statistics, such as e...

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Bibliographic Details
Published in2015 34th Chinese Control Conference (CCC) pp. 3880 - 3883
Main Author Huifeng, Ren
Format Conference Proceeding Journal Article
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2015
Subjects
Accuracy
Classification
Conferences
Entropy
Feature extraction
Feature fusion
Gain
Gray level co-occurrence matrix
Image classification
Image texture
Information gain
Mathematical models
Method of images
Noise
Statistics
Support vector machines
Surface layer
Texture
Texture feature
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Summary:The classical gray level co-occurrence matrix(GLCM) neglects the directional differences of texture. A novel method of image classification is presented in this paper. Multi-angle weighting of GLCM is proposed to decrease effects of directional difference, in which second-order statistics, such as energy, entropy, moment of inertia, moments of deficit and relevance, are calculated to describe image texture. The contributions to classifier of second-order statistics are evaluated using information gain. The model based on one against one support vector machines (OAOSVM) is realized. The experimental results has shown that the proposed method can accomplish image classification with higher accuracy on several standard image datasets than other methods.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2015.7260238