A Genetic Programming Approach to the Design of Interest Point Operators

  • Gustavo Olague
  • Leonardo Trujillo
Part of the Studies in Computational Intelligence book series (SCI, volume 256)


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.


Genetic Programming Evolutionary Computation Interest Point Interest Operator Pattern Recognition Letter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gustavo Olague
    • 1
  • Leonardo Trujillo
    • 2
  1. 1.Proyecto Evovisión, Departamento de Ciencias de la Computación, División de Física AplicadaCentro de Investigación Científica y de Educación Superior de EnsenadaEnsenadaMéxico
  2. 2.Instituto Tecnológico de TijuanaTijuanaMéxico

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