Improving Shape Retrieval by Integrating AIR and Modified MutualkNN Graph

Abstract

In computer vision, image retrieval remained a significant problem and recent resurgent of image retrieval also relies on other postprocessing methods to improve the accuracy instead of solely relying on good feature representation. Our method addressed the shape retrieval of binary images. This paper proposes a new integration scheme to best utilize feature representation along with contextual information. For feature representation we used articulation invariant representation; dynamic programming is then utilized for better shape matching followed by manifold learning based postprocessing modified mutualkNN graph to further improve the similarity score. We conducted extensive experiments on widely used MPEG-7 database of shape images by so-called bulls-eye score with and without normalization of modified mutualkNN graph which clearly indicates the importance of normalization. Finally, our method demonstrated better results compared to other methods. We also computed the computational time with another graph transduction method which clearly shows that our method is computationally very fast. Furthermore, to show consistency of postprocessing method, we also performed experiments on challenging ORL and YALE face datasets and improved baseline results.

A two-stage shape retrieval (TSR) method with global and local features

“…Post-processing techniques such as the diffusion process (DP) [10], [11], [12], [13], [14] have been proposed to compensate for errors arising from local-features-based shape retrieval methods. The DP treats each sample as a node and the similarity between any two samples corresponds to a weighted edge.…”