- author = "Leonardo Trujillo and Gustavo Olague",
- title = "Synthesis of interest point detectors through genetic programming",
- booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference on Genetic and evolutionary computation",
- year = "2006",
- editor = "Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos {Coello Coello} and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens",
- volume = "1",
- ISBN = "1-59593-186-4",
- pages = "887--894",
- address = "Seattle, Washington, USA",
- publisher = "ACM Press",
- publisher_address = "New York, NY, 10286-1405, USA",
- month = "8-12 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- note = "Bronze HUMIES winner",
- keywords = "genetic algorithms, genetic programming, feature representation, invariants, program synthesis, synthesis, theory",
- isbn13 = "1595931864",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2006/docs/p887.pdf",
-
URL = "https://human-competitive.org/sites/default/files/olague-paper-1-gecco-2006.pdf",
-
DOI = "
doi:10.1145/1143997.1144151",
- size = "8 pages",
- abstract = "This contribution presents a novel approach for the automatic generation of a low-level feature extractor that is useful in higher-level computer vision tasks. Specifically, our work centers on the well-known computer vision problem of interest point detection. We pose interest point detection as an optimization problem, and are able to apply Genetic Programming to generate operators that exhibit human-competitive performance when compared with state-of-the-art designs. This work uses the repeatability rate that is applied as a benchmark metric in computer vision literature as part of the GP fitness function, together with a measure of the entropy related with the point distribution across the image. This two measures promote geometric stability and global separability under several types of image transformations. This paper introduces a Genetic Programming implementation that was able to discover a modified version of the DET operator [Beaudet, 1978], that shows a surprisingly high-level of performance. In this work emphasis was given to the balance between genetic programming and domain knowledge expertise to obtain results that are equal or better than human created solutions.",
-
notes = "GECCO-2006 A joint meeting of the fifteenth
international conference on genetic algorithms
(ICGA-2006) and the eleventh annual genetic programming
conference (GP-2006).
ACM Order Number 910060
see also \cite{Olague:2006:sigevo}.",
