A Genetic Programming Approach to the Design of Interest Point Operators

Published in Book Chapters

  1. Gustavo Olague and Leonardo Trujillo. A Genetic Programming Approach to the Design of Interest Point Operators. In Patricia Melin, Janusz Kacprzyk and Witold Pedrycz (eds.). Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. Studies in Computational Intelligence series, volume 256, Springer Berlin Heidelberg, 2009, pages 49-65. URL, DOI BibTeX

    @incollection{,
    	year = 2009,
    	isbn = "978-3-642-04515-8",
    	booktitle = "Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition",
    	volume = 256,
    	series = "Studies in Computational Intelligence",
    	editor = "Melin, Patricia and Kacprzyk, Janusz and Pedrycz, Witold",
    	doi = "10.1007/978-3-642-04516-5_3",
    	title = "A Genetic Programming Approach to the Design of Interest Point Operators",
    	url = "http://dx.doi.org/10.1007/978-3-642-04516-5_3",
    	publisher = "Springer Berlin Heidelberg",
    	author = "Olague, Gustavo and Trujillo, Leonardo",
    	pages = "49-65"
    }
    

Abstract

Recently, the detection of local image feature has become an indispensable process for many image analysis or computer vision systems. In this chapter, we discuss how Genetic Programming (GP), a form of evolutionary search, can be used to automatically synthesize image operators that detect such features on digital images. The experimental results we review, confirm that artificial evolution can produce solutions that outperform many man-made designs. Moreover, we argue that GP is able to discover, and reuse, small code fragments, or building blocks, that facilitate the synthesis of image operators for point detection. Another noteworthy result is that the GP did not produce operators that rely on the auto-correlation matrix, a mathematical concept that some have considered to be the most appropriate to solve the point detection task. Hence, the GP generates operators that are conceptually simple and can still achieve a high performance on standard tests.

Published in
Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition
Studies in Computational Intelligence
Pages 49-65
Chapter 3
Volume 256
http://link.springer.com/chapter/10.1007%2F978-3-642-04516-5_3
Copyright
2009
ISSN
1860-949X
ISBN
978-3-642-04516-5
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