Unsupervised Learning for Edge Detection Using Genetic Programming

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

@InProceedings{Fu:2014:CEC,

• title = "Unsupervised Learning for Edge Detection Using Genetic Programming",
• author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
• pages = "117--124",
• booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation",
• year = "2014",
• month = "6-11 " # jul,
• editor = "Carlos A. {Coello Coello}",
• address = "Beijing, China",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, Genetic programming, Evolutionary Computer Vision",
• DOI = "doi:10.1109/CEC.2014.6900444",
• abstract = "In edge detection, a machine learning algorithm generally requires training images with their ground truth or designed outputs to train an edge detector. Meanwhile the computational cost is heavy for most supervised learning algorithms in the training stage when a large set of training images is used. To learn edge detectors without ground truth and reduce the computational cost, an unsupervised Genetic Programming (GP) system is proposed for low-level edge detection. A new fitness function is developed from the energy functions in active contours. The proposed GP system uses single images to evolve GP edge detectors, and these evolved edge detectors are used to detect edges on a large set of test images. The results of the experiments show that the proposed unsupervised learning GP system can effectively evolve good edge detectors to quickly detect edges on different natural images.",
• notes = "WCCI2014",

}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations