Multivariate texture retrieval using the Kullback-Leibler divergence between bivariate generalized Gamma times an Uniform distribution

• Published in: 2012 19th IEEE International Conference on Image Processing pp. 2413 - 2416
• Main Authors: Bombrun, L., Berthoumieu, Y.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2012; Institute of Electrical and Electronics Engineers
• Subjects: Computational modeling; Computer Science; Context; Engineering Sciences; Gaussian distribution; image retrieval; Indexing; Kullback-Leibler divergence; Maximum likelihood estimation; Multivariate elliptical distribution; Random variables; Signal and Image Processing; Texture; image retrieval; Multivariate elliptical distribution; Kullback-Leibler divergence; Texture
• ISBN: 1467325341; 9781467325349
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2012.6467384

This paper presents a new multivariate elliptical distribution, namely the multivariate generalized Gamma times an Uniform (MGΓU) distribution. Because it generalizes the multivariate generalized Gaussian distribution (MGGD), the MGΓU distribution is able to fit a wider range of signals. For the bivariate case, we provide a closed-form of the KullbackLeibler divergence (KLD). We propose the MGΓU distribution for modeling chrominance wavelet coefficients and exercise it in a texture retrieval experiment. A comparative study between some multivariate models on the VisTex and Outex image database is conducted and reveals that the use of the MGΓU distribution of chromiance wavelet coefficient allows an indexing gain compared to other classical approaches such as MGGD and Copula based model).