Genetic Programming for Edge Detection Based on Accuracy of Each Training Image
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @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