Exploiting disparity information for stereo image retrieval

The great interest of stereo images in several applications has led to the proliferation of huge and ever growing image databases. Therefore, there is an urgent demand for an effective Content Based Image Retrieval (CBIR) system devoted to stereo images. To meet such a demand, this paper proposes new wavelet-based retrieval approaches that exploit not only the visual contents of the Stereo Image (SI) pair but also its related disparity field. The first approach takes into account implicitly the disparity information by computing features from the disparity compensated left image and the right image. The second one aims at extracting relevant features directly from the left and right views, and the disparity map. Experimental results indicate that adding disparity information allows us to improve the retrieval performances of stereo images.