Image re-ranking and rank aggregation based on similarity of ranked lists

  • Authors:
  • Daniel Carlos Guimarães Pedronette;Ricardo da S. Torres

  • Affiliations:
  • RECOD Lab - Institute of Computing - University of Campinas, Campinas/SP - Brazil;RECOD Lab - Institute of Computing - University of Campinas, Campinas/SP - Brazil

  • Venue:
  • CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
  • Year:
  • 2011

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Abstract

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 reranking 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.