Texture Classification Using Kernel Independent Component Analysis

Authors:
Jian Cheng;Qingshan Liu;Hanqing Lu;Yen-Wei Chen
Affiliations:
Chinese Academy of Sciences, China;Chinese Academy of Sciences, China;Chinese Academy of Sciences, China;Ritsumeikan University, Japan
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Abstract

We propose a novel method, Kernel Independent Component Analysis (KICA), for texture features extraction. The texture images are first mapped into a higher-dimensional implicit feature space. Then a set of nonlinear basis functions are learned using KICA. The feature vectors are obtained by projected the texture images onto the basis functions. Comparison experiments between KICA and the other two classic methods: Gabor filters and ICA, are performed. The results indicate that the KICA is an efficient approach for texture classification.