Abstract
In this paper, we introduce the Cauchy-Schwarz divergence (CSD) in the context of texture retrieval. First, we model wavelet coefficients histograms using the already existing mixture of generalized Gaussians (MoGG) distribution. Then, we propose the CSD as a similarity measure between two MoGGs. As there is no closed-form of CSD, we compute this measure by a Monte-Carlo sampling method. Thanks to its tractable mathematical expression, CSD becomes computationally less expensive in contrast with Kullback-Leibler divergence (KLD). This later often needs other approximations with good sampling strategies or using bounding methods to avoid the heavy sampling process. Through the conducted experiments on two popular databases VisTeX and Brodatz, a retrieval rate of 98% is achieved.
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References
Mit vision and modelling group. texture database available at: http://vismod.media.mit.edu/vismod/imagery/visiontexture/vistex.html
Texture database available at: http://www.ux.uis.no/tranden/brodatz.html
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Rami, H., El Maliani, A.D., El Hassouni, M., Aboutajdine, D. (2014). Texture Retrieval Using Cauchy-Schwarz Divergence and Generalized Gaussian Mixtures. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_11
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DOI: https://doi.org/10.1007/978-3-319-14364-4_11
Publisher Name: Springer, Cham
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