Combining re-ranking and rank aggregation methods for image retrieval
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Data
2016-08-01
Autores
Pedronette, Daniel Carlos Guimarães [UNESP]
Torres, Ricardo da S.
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Resumo
This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.
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Content-based image retrieval, Fusion, Rank aggregation, Re-ranking
Como citar
Multimedia Tools and Applications, v. 75, n. 15, p. 9121-9144, 2016.