Learning to rank for content-based image retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Faria2010MIR,
  author =       "Fabio Augusto Faria and Adriano Veloso and 
                 Humberto {Mossri de Almeida} and Eduardo Valle and 
                 Ricardo {da S. Torres} and Marcos Andre Goncalves and 
                 Wagner {Meira Jr.}",
  title =        "Learning to rank for content-based image retrieval",
  booktitle =    "Multimedia Information Retrieval (MIR)",
  year =         "2010",
  pages =        "285--294",
  address =      "Philadelphia, Pennsylvania, USA",
  keywords =     "genetic algorithms, genetic programming, SVM",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://doi.acm.org/10.1145/1743384.1743434",
  DOI =          "doi:10.1145/1743384.1743434",
  abstract =     "In Content-based Image Retrieval (CBIR), accurately
                 ranking the returned images is of paramount importance,
                 since users consider mostly the topmost results. The
                 typical ranking strategy used by many CBIR systems is
                 to employ image content descriptors, so that returned
                 images that are most similar to the query image are
                 placed higher in the rank. While this strategy is well
                 accepted and widely used, improved results may be
                 obtained by combining multiple image descriptors. In
                 this paper we explore this idea, and introduce
                 algorithms that learn to combine information coming
                 from different descriptors. The proposed learning to
                 rank algorithms are based on three diverse learning
                 techniques: Support Vector Machines (CBIR-SVM), Genetic
                 Programming (CBIR-GP), and Association Rules (CBIR-AR).
                 Eighteen image content descriptors(colour, texture, and
                 shape information) are used as input and provided as
                 training to the learning algorithms. We performed a
                 systematic evaluation involving two complex and
                 heterogeneous image databases (Corel e Caltech) and two
                 evaluation measures (Precision and MAP). The empirical
                 results show that all learning algorithms provide
                 significant gains when compared to the typical ranking
                 strategy in which descriptors are used in isolation. We
                 concluded that, in general, CBIR-AR and CBIR-GP
                 outperforms CBIR-SVM. A fine-grained analysis revealed
                 the lack of correlation between the results provided by
                 CBIR-AR and the results provided by the other two
                 algorithms, which indicates the opportunity of an
                 advantageous hybrid approach.",
}

Genetic Programming entries for Fabio Augusto Faria Adriano Veloso Humberto Mossri de Almeida Eduardo Valle Ricardo da Silva Torres Marcos Andre Goncalves Wagner Meira

Citations