Image Descriptor: A Genetic Programming Approach to Multiclass Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Harith Al-Sahaf and Mengjie Zhang and 
                 Mark Johnston and Brijesh Verma",
  title =        "Image Descriptor: A Genetic Programming Approach to
                 Multiclass Texture Classification",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "2460--2467",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257190",
  abstract =     "Texture classification is an essential task in
                 computer vision that aims at grouping instances that
                 have a similar repetitive pattern into one group.
                 Detecting texture primitives can be used to
                 discriminate between materials of different types. The
                 process of detecting prominent features from the
                 texture instances represents a cornerstone step in
                 texture classification. Moreover, building a good model
                 using a few training instances is difficult. In this
                 study, a genetic programming (GP) descriptor is
                 proposed for the task of multiclass texture
                 classification. The proposed method synthesises a set
                 of mathematical formulas relying on the raw pixel
                 values and a sliding window of a predetermined size.
                 Furthermore, only two instances per class are used to
                 automatically evolve a descriptor that has the
                 potential to effectively discriminate between instances
                 of different textures using a simple instance-based
                 classifier to perform the classification task. The
                 performance of the proposed approach is examined using
                 two widely-used data sets, and compared with two
                 GP-based and nine well-known non-GP methods.
                 Furthermore, three hand-crafted domain-expert designed
                 feature extraction methods have been used with the
                 non-GP methods to examine the effectiveness of the
                 proposed method. The results show that the proposed
                 method has significantly outperformed all these other
                 methods on both data sets, and the new method evolves a
                 descriptor that is capable of achieving significantly
                 better performance compared to hand-crafted features.",
  notes =        "1340 hrs 15390 CEC2015",

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