Genetic Programming for Edge Detection Based on Accuracy of Each Training Image

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{conf/ausai/FuJZ11,

• author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
• title = "Genetic Programming for Edge Detection Based on Accuracy of Each Training Image",
• booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)",
• year = "2011",
• editor = "Dianhui Wang and Mark Reynolds",
• volume = "7106",
• series = "Lecture Notes in Computer Science",
• pages = "301--310",
• address = "Perth, Australia",
• month = dec # " 5-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1007/978-3-642-25832-9_31",
• size = "10 pages",
• 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.",
• affiliation = "School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand",
• bibdate = "2011-12-02",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#FuJZ11",

}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations