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Using contextual spaces for image re-ranking and rank aggregation

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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.

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Acknowledgements

Authors thank AMD, FAEPEX (grants 2007/52015-0 and 2009/18438-7), CAPES, FAPESP, and CNPq for financial support. Authors also thank DGA/UNICAMP for its support in this work.

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Authors and Affiliations

  1. Recod Lab - Institute of Computing, University of Campinas, Campinas, Brazil

    Daniel Carlos Guimarães Pedronette, Ricardo da Silva Torres & Rodrigo Tripodi Calumby

  2. Department of Exact Sciences, University of Feira de Santana, Feira de Santana, Brazil

    Rodrigo Tripodi Calumby

Authors
  1. Daniel Carlos Guimarães Pedronette
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  2. Ricardo da Silva Torres
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  3. Rodrigo Tripodi Calumby
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Correspondence to Daniel Carlos Guimarães Pedronette.

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Pedronette, D.C.G., da Silva Torres, R. & Calumby, R.T. Using contextual spaces for image re-ranking and rank aggregation. Multimed Tools Appl 69, 689–716 (2014). https://doi.org/10.1007/s11042-012-1115-z

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