Interest point detection through multiobjective genetic programming
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- @Article{Olague20122566,
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author = "Gustavo Olague and Leonardo Trujillo",
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title = "Interest point detection through multiobjective
genetic programming",
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journal = "Applied Soft Computing",
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volume = "12",
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number = "8",
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pages = "2566--2582",
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year = "2012",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2012.03.058",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494612001706",
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keywords = "genetic algorithms, genetic programming,
Multiobjective optimisation, Interest point detection,
Evolutionary computer vision",
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abstract = "The detection of stable and informative image points
is one of the most important low-level problems in
modern computer vision. This paper proposes a
multiobjective genetic programming (MO-GP) approach for
the automatic synthesis of operators that detect
interest points. The proposal is unique for interest
point detection because it poses a MO formulation of
the point detection problem. The search objectives for
the MO-GP search consider three properties that are
widely expressed as desirable for an interest point
detector, these are: (1) stability; (2) point
dispersion; and (3) high information content. The
results suggest that the point detection task is a MO
problem, and that different operators can provide
different trade-offs among the objectives. In fact,
MO-GP is able to find several sets of Pareto optimal
operators, whose performance is validated on
standardised procedures including an extensive test
with 500 images; as a result, we could say that all
solutions found by the system dominate previously
man-made detectors in the Pareto sense. In conclusion,
the MO formulation of the interest point detection
problem provides the appropriate framework for the
automatic design of image operators that achieve
interesting trade-offs between relevant performance
criteria that are meaningful for a variety of vision
tasks.",
- }
Genetic Programming entries for
Gustavo Olague
Leonardo Trujillo
Citations