PROJECT TITLE :

Adaptive learning region importance for region-based image retrieval

ABSTRACT:

This study addresses the problem of region illustration in region-based mostly image retrieval (RBIR). So as to cut back the user's burden of selecting the region of interest, a statistical index known as visual region importance (RI) is constructed to explain the region. By learning from user's current and historical feedback info, visual RI can be automatically updated and semantic RI can be obtained. Furthermore, adaptive learning RI and memory learning RI (MLRI) techniques for RBIR system have been presented. Specifically, the MLRI can mitigate the negative influence of interference regions well. In depth experiments on the Corel-a thousand dataset and therefore the Caltech-256 dataset demonstrate that the proposed frameworks are effective, are robust and achieve considerably higher performance than the opposite existing strategies.