Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7325

@InProceedings{lam:doh:gecco2004,

• author = "Brian Lam and Vic Ciesielski",
• title = "Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming",
• booktitle = "Genetic and Evolutionary Computation -- GECCO-2004, Part II",
• year = "2004",
• editor = "Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell",
• series = "Lecture Notes in Computer Science",
• pages = "1114--1125",
• address = "Seattle, WA, USA",
• publisher_address = "Heidelberg",
• month = "26-30 " # jun,
• organisation = "ISGEC",
• publisher = "Springer-Verlag",
• volume = "3103",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-22343-6",
• ISSN = "0302-9743",
• URL = "http://www.genetic-programming.org/gecco2004hc/lam-paper.pdf",
• URL = "https://researchrepository.rmit.edu.au/esploro/outputs/9921863828201341",
• DOI = "doi:10.1007/978-3-540-24855-2_121",
• size = "12 pages",
• abstract = "we show how genetic programming can be used to discover useful texture feature extraction algorithms. Grey level histograms of different textures are used as inputs to the evolved programs. One dimensional K-means clustering is applied to the outputs and the tightness of the clusters is used as the fitness measure. To test generality, textures from the Brodatz library were used in learning phase and the evolved features were used on classification problems based on the Vistex library. Using the evolved features gave a test accuracy of 74.8percent while using Haralick features, the most commonly used method in texture classification, gave an accuracy of 75.5percent on the same problem. Thus, the evolved features are competitive with those derived by human intuition and analysis. Furthermore, when the evolved features are combined with the Haralick features the accuracy increases to 83.2percent, indicating that the evolved features are finding texture regularities not used in the Haralick approach.",
• notes = "GECCO-2004 A joint meeting of the thirteenth international conference on genetic algorithms (ICGA-2004) and the ninth annual genetic programming conference (GP-2004)",

}

Genetic Programming entries for Brian Lam Victor Ciesielski

Citations