Unsupervised manifold learning using Reciprocal kNN Graphs in image re-ranking and rank aggregation tasks

  • Authors:

  • Daniel Carlos Guimarães Pedronette;Otávio A. B. Penatti;Ricardo Da S. Torres

  • Affiliations:

  • Department of Statistics, Applied Mathematics and Computing, Universidade Estadual Paulista (UNESP), Av. 24-A, 1515, Rio Claro, SP, 13506-900, Brazil;RECOD Lab, Institute of Computing (IC), University of Campinas (Unicamp), Av. Albert Einstein, 1251, Campinas, SP, 13083-852, Brazil and SAMSUNG Research Institute, Av Cambacicas, 1200, Campinas, ...;RECOD Lab, Institute of Computing (IC), University of Campinas (Unicamp), Av. Albert Einstein, 1251, Campinas, SP, 13083-852, Brazil

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2014

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Abstract

In this paper, we present an unsupervised distance learning approach for improving the effectiveness of image retrieval tasks. We propose a Reciprocal kNN Graph algorithm that considers the relationships among ranked lists in the context of a k-reciprocal neighborhood. The similarity is propagated among neighbors considering the geometry of the dataset manifold. The proposed method can be used both for re-ranking and rank aggregation tasks. Unlike traditional diffusion process methods, which require matrix multiplication operations, our algorithm takes only a subset of ranked lists as input, presenting linear complexity in terms of computational and storage requirements. We conducted a large evaluation protocol involving shape, color, and texture descriptors, various datasets, and comparisons with other post-processing approaches. The re-ranking and rank aggregation algorithms yield better results in terms of effectiveness performance than various state-of-the-art algorithms recently proposed in the literature, achieving bull's eye and MAP scores of 100% on the well-known MPEG-7 shape dataset.