Diversity-driven learning for multimodal image retrieval with relevance feedback

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "R. T. Calumby and R. {da Silva Torres} and 
                 M. A. Goncalves",
  booktitle =    "IEEE International Conference on Image Processing
                 (ICIP 2014)",
  title =        "Diversity-driven learning for multimodal image
                 retrieval with relevance feedback",
  year =         "2014",
  month =        oct,
  pages =        "2197--2201",
  abstract =     "We introduce a new genetic programming approach for
                 enhancing the user search experience based on relevance
                 feedback over results produced by a multimodal image
                 retrieval technique with explicit diversity promotion.
                 We have studied maximal marginal relevance re-ranking
                 methods for result diversification and their impacts on
                 the overall retrieval effectiveness. We show that the
                 learning process using diverse results may improve user
                 experience in terms of both the number of relevant
                 items retrieved and subtopic coverage.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICIP.2014.7025445",
  notes =        "Also known as \cite{7025445}",

Genetic Programming entries for Rodrigo Tripodi Calumby Ricardo da Silva Torres Marcos Andre Goncalves