- author = "Leonardo Trujillo and Gustavo Olague and Evelyne Lutton and Francisco {Fernandez de Vega}",
- title = "Multiobjective design of operators that detect points of interest in images",
- booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation",
- year = "2008",
- editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener",
- isbn13 = "978-1-60558-130-9",
- pages = "1299--1306",
- address = "Atlanta, GA, USA",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "12-16 " # jul,
- keywords = "genetic algorithms, genetic programming, MOGP, interest point detection, multiobjective optimisation, Algorithms, Experimentation, Performance, Theory, Multiobjective optimization, Interest point detection",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p1299.pdf",
-
DOI = "
doi:10.1145/1389095.1389344",
- size = "8 pages",
- 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}",
