Image feature selection using genetic programming for figure-ground segmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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",
• year = "2017",
• volume = "62",
• pages = "96--108",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Figure-ground segmentation, Feature selection, Multi-objective methods",
• ISSN = "0952-1976",
• URL = "http://www.sciencedirect.com/science/article/pii/S0952197617300544",
• DOI = "doi:10.1016/j.engappai.2017.03.009",
• 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.",
• notes = "Also known as \cite{LIANG201796}",

}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

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