Disparity based stereo image retrieval through univariate and bivariate models

• Published in: Signal processing. Image communication Vol. 31; pp. 174 - 184
• Main Authors: Chaker, A., Kaaniche, M., Benazza-Benyahia, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2015; Elsevier
• Subjects: Content based image retrieval; Correlation; Design engineering; Disparity map; Engineering Sciences; Feature extraction; Gaussian; Mathematical models; Retrieval; Statistical analysis; Stereo pairs; Visual; Wavelet domain; Content based image retrieval; Disparity map; Feature extraction; Wavelet domain; Stereo pairs
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2014.12.004

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. •We design efficient techniques for stereo image retrieval.•The developed methods are based on univariate and bivariate statistical models.•We show the benefits which can be drawn from incorporating the disparity information in the retrieval step.