Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing the Rao distance for gamma distributions
Journal of Computational and Applied Mathematics
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Copulas based multivariate gamma modeling for texture classification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Characterising Facial Gender Difference Using Fisher-Rao Metric
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
International Journal of Computer Vision
Color texture classification using rao distance between multivariate copula based models
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework
IEEE Transactions on Image Processing
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This paper concerns multicomponent texture classification. The aim is to provide a flexible model when wavelet subband coefficients of components do not have the same distributions. Example of such case is when color textures are represented in a perceptual color space. In this kind of representation, the separability between luminance and chrominance components have to be considered in the modeling process. The contribution of this work consists in proposing a multi-model based characterization for this type of multicomponent images. For this, two models ML and MCr are used in order to extract features from luminance and chrominance components, respectively. We discuss in detail and define the multi-model when textures are represented in the HSV color space as a special case of multicomponent analysis. Experimental results show that the proposed approach improves performances of the classification system when compared with existing methods.