Combining Re-Ranking and Rank Aggregation Methods

  • Daniel Carlos Guimarães Pedronette
  • Ricardo da S. Torres
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Content-Based Image Retrieval (CBIR) aims at retrieving 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 produce different image rankings. These rankings are complementary and, therefore, can be further combined aiming at obtaining more effective results. This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of CBIR systems. Several experiments were conducted involving shape, color, and texture descriptors. Experimental results demonstrate that our approaches can improve the effectiveness of CBIR systems.


Image Retrieval Combination Approach Mean Average Precision Image Descriptor CBIR System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Carlos Guimarães Pedronette
    • 1
  • Ricardo da S. Torres
    • 1
  1. 1.Recod Lab - Institute of ComputingUniversity of CampinasCampinasBrazil

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