Using contextual spaces for image re-ranking and rank aggregation

Authors:
Daniel Carlos Pedronette;Ricardo Da Silva Torres;Rodrigo Tripodi Calumby
Affiliations:
Recod Lab - Institute of Computing, University of Campinas, Campinas, Brazil;Recod Lab - Institute of Computing, University of Campinas, Campinas, Brazil;Recod Lab - Institute of Computing, University of Campinas, Campinas, Brazil and Department of Exact Sciences, University of Feira de Santana, Feira de Santana, Brazil
Venue:
Multimedia Tools and Applications
Year:
2014

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Abstract

This article presents two novel re-ranking approaches that take into account contextual information defined by the K-Nearest Neighbours (KNN) of a query image for improving the effectiveness of CBIR systems. The main contributions of this article are the definition of the concept of contextual spaces for encoding contextual information of images; the definition of two new re-ranking algorithms that exploit contextual information encoded in contextual spaces; and the evaluation of the proposed algorithms in several CBIR tasks related to the combination of image descriptors; combination of visual and textual descriptors; and combination of post-processing (re-ranking) methods. We conducted a large evaluation protocol involving visual descriptors (considering shape, color, and texture) and textual descriptors, various datasets, and comparisons with other post-processing methods. Experimental results demonstrate the effectiveness of our approaches.