Feature selection and fusion for texture classification

Authors:
Shutao Li;Yaonan Wang
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha, Hunan and National Laboratory on Machine Perception, Peking University, Beijing, China;College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

Citing 5
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Abstract

In this paper, a novel texture classification method using selected and combined features from wavelet frame and steerable pyramid decompositions has been proposed. Firstly, wavelet frame and steerable pyramid decompositions are used to extract complementary features from texture regions. Then the number of features is reduced by selection using maximal information compression index. Finally the reduced features are combined and forwarded to SVM classifiers. The experimental results show that the proposed method used selected and fused features can achieve good classification accuracy and have low computational complexity.