Learning Figure-ground Image Segmentors by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Liang:2017:GECCO,
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author = "Yuyu Liang and Mengjie Zhang and Will N. Browne",
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title = "Learning Figure-ground Image Segmentors by Genetic
Programming",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "239--240",
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size = "2 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3075989",
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DOI = "doi:10.1145/3067695.3075989",
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acmid = "3075989",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, figure-ground
segmentation, postprocessing, preprocessing",
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month = "15-19 " # jul,
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abstract = "Figure-ground segmentation is an important image
processing task that genetic programming (GP) has been
successfully introduced to solve. However, existing GP
methods use a homogeneous mixture of preprocessing and
post processing operators for segmentation. This can
result in inappropriate operators being connected,
leading to poor performance and unnecessary operations
in solutions. To address this issue, two new methods
are designed to enable GP to conduct image
preprocessing, binarisation and postprocessing
separately. Specifically, the two methods introduce a
strongly-typed representation (StronglyGP) and a
two-stage evolution (TwostageGP) in GP respectively
Results show that StronglyGP can evolve effective
segmentors for the given complex segmentation tasks.
However, TwostageGP currently performs poorly, which is
likely caused by over fitting, which will be addressed
in future work.",
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notes = "Also known as \cite{Liang:2017:LFI:3067695.3075989}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Yuyu Liang
Mengjie Zhang
Will N Browne
Citations