DocumentCode :
2779092
Title :
Genetic programming for edge detection via balancing individual training images
Author :
Fu, Wenlong ; Johnston, Mark ; Zhang, Mengjie
Author_Institution :
Sch. of Math., Stat. & Oper. Res., Victoria Univ. of Wellington, Wellington, New Zealand
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
8
Abstract :
Edge detectors trained by a machine learning algorithm are usually evaluated by the accuracy based on overall pixels in the training stage, rather than the information for each training image. However, when the evaluation for training edge detectors considers the accuracy of each image, the influence on the final detectors has not been investigated. In this study, we employ genetic programming to evolve detectors with new fitness functions containing the accuracy of training images. The experimental results show that fitness functions based on the accuracy of single training images can balance the accuracies across detection results, and the fitness function combining the accuracy of overall pixels with the accuracy of training images together can improve the detection performance.
Keywords :
edge detection; genetic algorithms; learning (artificial intelligence); edge detection; fitness functions; genetic programming; individual training image balancing; machine learning algorithm; overall pixel accuracy; training image accuracy; Accuracy; Detectors; Feature extraction; Genetic programming; Image edge detection; Training; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
Type :
conf
DOI :
10.1109/CEC.2012.6252879
Filename :
6252879
Link To Document :
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