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Proceedings Paper

On the selection of ICA features for texture classification
Author(s): Xuezhi Yang; DeMei Yang; Jing Shen
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960S (30 October 2009); doi: 10.1117/12.832376
Show Author Affiliations
Xuezhi Yang, Hefei Univ. of Technology (China)
DeMei Yang, Hefei Univ. of Technology (China)
Jing Shen, Hefei Normal Univ. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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