Learning invariant region descriptor operators with genetic programming and the F-measure
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DBLP:conf/icpr/PerezO08,
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author = "Cynthia B. Perez and Gustavo Olague",
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title = "Learning invariant region descriptor operators with
genetic programming and the {F}-measure",
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booktitle = "19th International Conference on Pattern Recognition
(ICPR 2008)",
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year = "2008",
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pages = "1--4",
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address = "Tampa, Florida, USA",
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month = dec # " 8-11",
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keywords = "genetic algorithms, genetic programming, GPLAB",
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isbn13 = "978-1-4244-2175-6",
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DOI = "doi:10.1109/ICPR.2008.4761178",
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size = "4 pages",
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abstract = "Recognizing and localizing objects is a classical
problem in computer vision that is an important stage
for many automated systems. In order to perform object
recognition many researchers have focused on local
features as the basis of their proposed methodologies.
This work is devoted to the task of learning invariant
region descriptor operators with genetic programming.
The idea is to find a set of expressions that could be
equal or better than the weighted gradient magnitude
that is normally applied on the SIFT descriptor. This
magnitude corresponds to the operator that we would
like to improve through genetic programming (GP). The
key for a successful problem statement was achieved
with the F-measure. After a bibliographical study we
have found a criterion that is simple, reliable, and
useful in the estimation of such a metric. The measure
that we propose here is based on the harmonic mean
which is normally used by the information retrieval
community. Experimental results show that the evolved
descriptor's operator can enhance significantly the
overall performance of the SIFT descriptor and surpass
other state-of-the-art algorithms.",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Cynthia B Perez
Gustavo Olague
Citations