Image feature selection using genetic programming for figure-ground segmentation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Liang:2017:EAAI,
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author = "Yuyu Liang and Mengjie Zhang and Will N. Browne",
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title = "Image feature selection using genetic programming for
figure-ground segmentation",
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journal = "Engineering Applications of Artificial Intelligence",
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year = "2017",
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volume = "62",
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pages = "96--108",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Figure-ground
segmentation, Feature selection, Multi-objective
methods",
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ISSN = "0952-1976",
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URL = "http://www.sciencedirect.com/science/article/pii/S0952197617300544",
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DOI = "doi:10.1016/j.engappai.2017.03.009",
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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.",
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notes = "Also known as \cite{LIANG201796}",
- }
Genetic Programming entries for
Yuyu Liang
Mengjie Zhang
Will N Browne
Citations