On the selection of ICA features for texture classification

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE2009Vol. 7496, pp. 74960S–74960S

Xuezhi Yang, DeMei Yang, Jing Shen

Abstract

Independent component analysis (ICA) provides an efficient approach to characterizing higher-order statistical relationships in texture images. For the classification of textures based on ICA, a fundamental problem lies on the selection of ICA features which are desired to maximize the separability between classes. In this paper, the efficiency of various ICA features for texture classification is investigated, which involves ICA coefficients and their various statistics with a focus on the higher-order statistics to take into account the non-Gaussian property of ICA coefficients. By evaluating the ICA features on the classification of twenty-five classes of Brodatz texture images, it has been shown that the higher-order statistics of ICA coefficients offer efficient discrimination of textures and the combination of variance, skewness and kurtosis is a better alternative to the previously reported ICA features. By comparing the performance of ICA features to their principal component analysis (PCA) counterparts, it is further revealed that the advantage of ICA for texture classification can be obtained by using the higher-order statistics of ICA coefficients.

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