Computing the Rao distance for gamma distributions
Journal of Computational and Applied Mathematics
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Modified Kernel functions by geodesic distance
EURASIP Journal on Applied Signal Processing
Digital Signal Processing
A joint model of complex wavelet coefficients for texture retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Multi-model approach for multicomponent texture classification
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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This paper presents a new similarity measure based on Rao distance for color texture classification or retrieval. Textures are characterized by a joint model of complex wavelet coefficients. This model is based on a Gaussian Copula in order to consider the dependency between color components. Then, a closed form of Rao distance is computed to measure the difference between two Gaussian Copula based probabilty density functions on the corresponding manifold. Results in term of classification rates, show the effectiveness of the Rao geodesic distance when applied on the manifold of Gaussian Copula based probability distributions, in comparison with the Kullback-Leibler divergence.