Image feature selection using genetic programming for figure-ground segmentation

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

@Article{Liang:2017:EAAI,
  author =       "Yuyu Liang and Mengjie Zhang and Will N. Browne",
  title =        "Image feature selection using genetic programming for
                 figure-ground segmentation",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "62",
  pages =        "96--108",
  year =         "2017",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2017.03.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0952197617300544",
  abstract =     "Figure-ground segmentation is the process of
                 separating regions of interest from unimportant
                 background. One challenge is to segment images with
                 high variations (e.g. containing a cluttered
                 background), which requires effective feature sets to
                 capture the distinguishing information between objects
                 and backgrounds. Feature selection is necessary to
                 remove noisy/redundant features from those extracted by
                 image descriptors. As a powerful search algorithm,
                 genetic programming (GP) is employed for the first time
                 to build feature selection methods that aims to improve
                 the segmentation performance of standard classification
                 techniques. Both single-objective and multi-objective
                 GP techniques are investigated, based on which three
                 novel feature selection methods are proposed.
                 Specifically, one method is single-objective, called
                 PGP-FS (parsimony GP feature selection); while the
                 other two are multi-objective, named nondominated
                 sorting GP feature selection (NSGP-FS) and strength
                 Pareto GP feature selection (SPGP-FS). The feature
                 subsets produced by the three proposed methods, two
                 standard sequential selection algorithms, and the
                 original feature set are tested via standard
                 classification algorithms on two datasets with high
                 variations (the Weizmann and Pascal datasets). The
                 results show that the two multi-objective methods
                 (NSGP-FS and SPGP-FS) can produce feature subsets that
                 lead to solutions achieving better segmentation
                 performance with lower numbers of features than the
                 sequential algorithms and the original feature set
                 based on standard classifiers for given segmentation
                 tasks. In contrast, PGP-FS produces results that are
                 not consistent for different classifiers. This
                 indicates that the proposed multi-objective methods can
                 help standard classifiers improve the segmentation
                 performance while reducing the processing time.
                 Moreover, compared with SPGP-FS, NSGP-FS is equally
                 capable of producing effective feature subsets, yet is
                 better at keeping diverse solutions.",
  keywords =     "genetic algorithms, genetic programming, Figure-ground
                 segmentation, Feature selection, Multi-objective
                 methods",
}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

Citations