Open Access

A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming

EURASIP Journal on Advances in Signal Processing20032003:206791

Received: 30 June 2002

Published: 21 July 2003


This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.


machine learning neural networks genetic algorithms object recognition target detection computer vision

Authors’ Affiliations

School of Mathematical and Computing Sciences, Victoria University of Wellington
School of Computer Science and Information Technology, RMIT University


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