Combining re-ranking and rank aggregation methods for image retrieval

In this paper, entitled “Combining re-ranking and rank aggregation methods for image retrieval”, the authors Daniel Pedronette and Ricardo Torres propose four novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of CBIR systems. They discuss how to combine (i) re-ranking algorithms; (ii) rank aggregation algorithms, and both (iii) re-ranking and rank aggregation algorithms.

The proposed approaches are evaluated considering visual (shape, color, and texture descriptors) and multimodal retrieval (visual and textual descriptors). Experimental results demonstrate that their combination approaches can further improve the effectiveness of image retrieval systems.


Daniel Carlos Pedronette and Ricardo Da Torres. 2016. Combining re-ranking and rank aggregation methods for image retrieval. Multimedia Tools Appl. 75, 15 (August 2016), 9121-9144. DOI: http://dx.doi.org/10.1007/s11042-015-3044-0

Main steps of the Contextual Re-ranking algorithm

Main steps of the Contextual Re-ranking algorithm

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