Distribution-based invariant feature construction using genetic programming for edge detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Fu:2015:SC,
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author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
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title = "Distribution-based invariant feature construction
using genetic programming for edge detection",
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journal = "Soft Computing",
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year = "2015",
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volume = "19",
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number = "8",
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pages = "2371--2389",
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month = aug,
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keywords = "genetic algorithms, genetic programming, SVM, Edge
detection, Distribution estimation, Feature
extraction",
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ISSN = "1432-7643",
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DOI = "doi:10.1007/s00500-014-1432-4",
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size = "19 pages",
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abstract = "In edge detection, constructing features with rich
responses on different types of edges is a challenging
problem. Genetic programming (GP) has been previously
employed to construct features. Normally, the values of
the features constructed by GP are calculated from raw
observations. Some existing work has considered the
distributions of the raw observations, but these
features only poorly indicate class label
probabilities. To construct features with rich
responses on different types of edges, the
distributions of the observations from GP programs are
investigated in this study. The values of the
constructed features are obtained from estimated
distributions, rather than directly using the
observations. These features themselves indicate
probabilities for the target labels. Basic
rotation-invariant features from gradients, image
quality, and local histograms are used to construct new
composite features. The results show that the invariant
features constructed by GP combine advantages from the
basic features, reduce drawbacks from basic features
alone, and improve the detection performance. In terms
of the quantitative and qualitative evaluations,
features constructed by GP with distribution estimation
are better than the combinations from a Bayesian model
and a linear support vector machine approach.",
- }
Genetic Programming entries for
Wenlong Fu
Mark Johnston
Mengjie Zhang
Citations