Genetic programming for edge detection based on accuracy of each training image

Authors:
Wenlong Fu;Mark Johnston;Mengjie Zhang
Affiliations:
School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

Citing 12
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Abstract

This paper investigates fitness functions based on the detecting accuracy of each training image. In general, machine learning algorithms for edge detection only focus on the accuracy based on all training pixels treated equally, but the accuracy based on every training image is not investigated. We employ genetic programming to evolve detectors with fitness functions based on the accuracy of every training image. Here, average (arithmetic mean) and geometric mean are used as fitness functions for normal natural images. The experimental results show fitness functions based on the accuracy of each training image obtain better performance, compared with the Sobel detector, and there is no obvious difference between the fitness functions with average and geometric mean.