Disparity based stereo image retrieval through univariate and bivariate models

The widespread use of stereovision in various application fields has led to the constitution of very huge stereo image databases. Therefore, the design of effective content based image retrieval system devoted to stereo pairs becomes an issue of importance. To this end, we propose in this paper two retrieval methods which combine the visual contents of the stereo images with their corresponding disparity information. After modeling the distribution of their associated wavelet coefficients by the generalized Gaussian statistical model, the resulting distribution parameters are selected as salient features. While the two views are processed separately through a univariate modeling in the first method, the second one exploits the correlation between the views by resorting to a bivariate modeling. Experimental results show the benefits which can be drawn from the proposed retrieval approaches.