Genetic Programming Evolved Filters from a Small Number of Instances for Multiclass Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/ivcnz/Al-SahafZJ14,
  author =       "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
  title =        "Genetic Programming Evolved Filters from a Small
                 Number of Instances for Multiclass Texture
                 Classification",
  booktitle =    "Proceedings of the 29th International Conference on
                 Image and Vision Computing New Zealand, {IVCNZ} 2014",
  publisher =    "ACM",
  year =         "2014",
  editor =       "Michael J. Cree and Lee V. Streeter and 
                 John Perrone and Michael Mayo and Anthony M. Blake",
  pages =        "84--89",
  address =      "Hamilton, New Zealand",
  month =        nov # " 19-21",
  keywords =     "genetic algorithms, genetic programming, Multiclass
                 classification, Textures",
  isbn13 =       "978-1-4503-3184-5",
  bibdate =      "2015-01-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/ivcnz/ivcnz2014.html#Al-SahafZJ14",
  DOI =          "doi:10.1145/2683405.2683418",
  acmid =        "2683418",
  abstract =     "Texture classification is an essential task in pattern
                 recognition and computer vision. In this paper, a novel
                 genetic programming (GP) based method is proposed for
                 the task of multiclass texture classification. The
                 proposed method evolves a set of filters using only two
                 instances per class. Moreover, the evolved program
                 operates directly on the raw pixel values and does not
                 require human intervention to perform feature selection
                 and extraction. Two well-known and widely used data
                 sets are used in this study to evaluate the performance
                 of the proposed method. The performance of the new
                 method is compared to that of two GP-based methods
                 using the raw pixel values, and six non-GP methods
                 using three different sets of domain-specific features.
                 The results show that the proposed method has
                 significantly outperformed the other methods on both
                 data sets.",
  URL =          "http://dl.acm.org/citation.cfm?id=2683405",
}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

Citations