abstract = "In this paper, a multiobjective (MO) learning approach
to image feature extraction is described, where
Pareto-optimal interest point (IP) detectors are
synthesized using genetic programming (GP). IPs are
image pixels that are unique, robust to changes during
image acquisition, and convey highly descriptive
information. Detecting such features is ubiquitous to
many vision applications, e.g. object recognition,
image indexing, stereo vision, and content based image
retrieval. In this work, candidate IP operators are
automatically synthesized by the GP process using
simple image operations and arithmetic functions. Three
experimental optimization criteria are considered: 1)
the repeatability rate; 2) the amount of global
separability between IPs; and 3) the information
content captured by the set of detected IPs. The MO-GP
search considers Pareto dominance relations between
candidate operators, a perspective that has not been
contemplated in previous research devoted to this
problem. The experimental results suggest that IP
detection is an illposed problem for which a single
globally optimum solution does not exist. We conclude
that the evolved operators outperform and dominate, in
the Pareto sense, all previously man-made designs.",
notes = "GECCO-2008 A joint meeting of the seventeenth
international conference on genetic algorithms
(ICGA-2008) and the thirteenth annual genetic
programming conference (GP-2008).
ACM Order Number 910081. Also known as \cite{1389344}",