Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{daSilva2012510,
  author =       "Andre Tavares {da Silva} and 
                 Jefersson Alex {dos Santos} and Alexandre Xavier Falcao and 
                 Ricardo {da S. Torres} and Leo Pini Magalhaes",
  title =        "Incorporating multiple distance spaces in optimum-path
                 forest classification to improve feedback-based
                 learning",
  journal =      "Computer Vision and Image Understanding",
  volume =       "116",
  number =       "4",
  pages =        "510--523",
  year =         "2012",
  ISSN =         "1077-3142",
  DOI =          "doi:10.1016/j.cviu.2011.12.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S107731421100261X",
  keywords =     "genetic algorithms, genetic programming, Content-based
                 image retrieval, Optimum-path forest classifiers,
                 Composite descriptor, Multi-scale parameter search,
                 Image pattern analysis",
  abstract =     "In content-based image retrieval (CBIR) using
                 feedback-based learning, the user marks the relevance
                 of returned images and the system learns how to return
                 more relevant images in a next iteration. In this
                 learning process, image comparison may be based on
                 distinct distance spaces due to multiple visual content
                 representations. This work improves the retrieval
                 process by incorporating multiple distance spaces in a
                 recent method based on optimum-path forest (OPF)
                 classification. For a given training set with relevant
                 and irrelevant images, an optimisation algorithm finds
                 the best distance function to compare images as a
                 combination of their distances according to different
                 representations. Two optimisation techniques are
                 evaluated: a multi-scale parameter search (MSPS), never
                 used before for CBIR, and a genetic programming (GP)
                 algorithm. The combined distance function is used to
                 project an OPF classifier and to rank images classified
                 as relevant for the next iteration. The ranking process
                 takes into account relevant and irrelevant
                 representatives, previously found by the OPF
                 classifier. Experiments show the advantages in
                 effectiveness of the proposed approach with both
                 optimisation techniques over the same approach with
                 single distance space and over another state-of-the-art
                 method based on multiple distance spaces.",
}

Genetic Programming entries for Andre Tavares da Silva Jefersson Alex dos Santos Alexandre X Falcao Ricardo da Silva Torres Leo Pini Magalhaes

Citations