期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 25, 期 7, 页码 1717-1725出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2014.06.004
关键词
Color texture; Wavelet representation; Multivariate copula model; Rao geodesic distance; Bayesian classification; Lab and HSV color spaces; Dependence structure; Joint linear-circular model
In this paper, we propose a novel color texture classification method based on statistical characterization. The approach consists in modeling complex wavelet coefficients of both luminance and chrominance components separately leading to a multi-modeling approach. The copula theory allows to take into account the spatial dependencies which exist within the intra-luminance sub-bands via the luminance model M-L, and also between the inter-chrominance subband coefficients via the chrominance model M-CT The multi-model, i.e. M-L and M-CT, is used to develop a Bayesian classifier based on the softmax principal. To derive the classifier, we propose a closed-form expression for the Rao geodesic distance between two copulas. Experiments on two sub-families of luminance-chrominance color spaces namely Lab and HSV have been carried out for a wide range of color texture databases. The combination of different statistical sub-models show that the multi-modeling performs better than some existing methods in term of classification rates. (C) 2014 Elsevier Inc. All rights reserved.
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