Localisation fitness in GP for object detection

  • Authors:

  • Mengjie Zhang;Malcolm Lett

  • Affiliations:

  • School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, Wellington, New Zealand

  • Venue:
  • EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
  • Year:
  • 2006

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Abstract

This paper describes two new fitness functions in genetic programming for object detection particularly object localisation problems. Both fitness functions use weighted F-measure of a genetic program and consider the localisation fitness values of the detected object locations, which are the relative weights of these locations to the target object centers. The first fitness function calculates the weighted localisation fitness of each detected object, then uses these localisation fitness values of all the detected objects to construct the final fitness of a genetic program. The second fitness function calculates the average locations of all the detected object centres then calculates the weighted localisation fitness value of the averaged position. The two fitness functions are examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that almost all the objects of interest in the large images can be successfully detected by all the three fitness functions, but the two new fitness functions can result in far fewer false alarms and spend much less training time.