Exploiting contextual information for rank aggregation

This paper presents a novel rank aggregation approach based on contextual information aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) tasks. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information and then re-rank collection images. We conducted several experiments involving shape, color, and texture descriptors. We also evaluated our method in comparison to other rank aggregation approaches. Experimental results demonstrate the effectiveness of our method.