A comparison of genetic programming feature extraction languages for image classification
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- @InProceedings{Maghoumi:2014:CIMSIVP,
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author = "M. Maghoumi and B. J. Ross",
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booktitle = "IEEE Symposium on Computational Intelligence for
Multimedia, Signal and Vision Processing (CIMSIVP
2014)",
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title = "A comparison of genetic programming feature extraction
languages for image classification",
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year = "2014",
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month = dec,
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abstract = "Visual pattern recognition and classification is a
challenging computer vision problem. Genetic
programming has been applied towards automatic visual
pattern recognition. One of the main factors in
evolving effective classifiers is the suitability of
the GP language for defining expressions for feature
extraction and classification. This research presents a
comparative study of a variety of GP languages suitable
for classification. Four different languages are
examined, which use different selections of image
processing operators. One of the languages does block
classification, which means that an image is classified
as a whole by examining many blocks of pixels within
it. The other languages are pixel classifiers, which
determine classification for a single pixel. Pixel
classifiers are more common in the GP-vision
literature. We tested the languages on different
instances of Brodatz textures, as well as aerial and
camera images. Our results show that the most effective
languages are pixel-based ones with spatial operators.
However, as is to be expected, the nature of the image
will determine the effectiveness of the language
used.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CIMSIVP.2014.7013278",
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notes = "Also known as \cite{7013278}",
- }
Genetic Programming entries for
M Maghoumi
Brian J Ross
Citations