The objective of Content-based Image Retrieval (CBIR) systems is to return a ranked list containing the most similar images in a collection given a query image. The effectiveness of these systems is very dependent on the accuracy of the distance function adopted. In this paper, we present a novel approach for redefining distances and later re-ranking images aiming to improve the effectiveness of CBIR systems. In our approach, distance among images are redefined based on the similarity of their ranked lists. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.
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