Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy Schwarz divergence in wavelet domain
This paper presents a novel similarity measure in a texture retrieval framework based on statistical modeling in wavelet domain. In this context, we use the recently proposed finite mixture of generalized Gaussian distribution (MoGG) thanks to its ability to model accurately a wide range of wavelet sub bands histograms. This model has already been relied on the approximation of Kullback Leibler divergence (KLD) which hinders significantly the retrieval process. To overcome this drawback, we introduce the Cauchy Schwarz divergence (CSD) between two MoGG distributions as a similarity measure. Hence, an analytic closed form expression of this measure is developed in the case of fixed shape parameter. Otherwise, when the shape parameter is variable, two approximations are derived using the well known stochastic integration with Monte Carlo simulations and numerical integration with Simpson's rule. Experiments conducted on a well known dataset show good performance of the CSD in terms of retrieval rates and the computational time improvement compared to the KLD. HighlightsWe propose Cauchy Schwarz divergence CSD for texture retrieval.Wavelet coefficients are modeled by mixture of generalized Gaussians MoGG.We derive a closed form of CSD between MoGG for fixed parameter shape.We use the Monte Carlo approximation for CSD in the general case.We evaluate the performance in terms of the average retrieval rates and the computational time.
Signal Process. Image Commun.