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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Genetic Programming for Edge Detection Based on Accuracy of Each Training Image
verfasst von : Wenlong Fu, Mark Johnston, Mengjie Zhang
Erschienen in: AI 2011: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.