Binary image classification using genetic programming based on local binary patterns

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

@InProceedings{Al-Sahaf:2013:IVCNZ,

• author = "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
• title = "Binary image classification using genetic programming based on local binary patterns",
• booktitle = "28th International Conference of Image and Vision Computing New Zealand (IVCNZ 2013)",
• year = "2013",
• pages = "220--225",
• address = "Wellington",
• month = nov,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, computer vision, image classification, learning (artificial intelligence), statistical analysis, ANOVA, GP based methods, LBP, SVM, binary image classification, computer vision, image descriptor, learning instances, local binary patterns, machine learning, nonGP methods, one-way analysis of variance, support vector machine, wrapped classifiers, Accuracy, Analysis of variance, Feature extraction, Histograms, Support vector machines, Training, Vectors",
• DOI = "doi:10.1109/IVCNZ.2013.6727019",
• size = "6 pages",
• abstract = "Image classification represents an important task in machine learning and computer vision. To capture features covering a diversity of different objects, it has been observed that a sufficient number of learning instances are required to efficiently estimate the models' parameter values. In this paper, we propose a genetic programming (GP) based method for the problem of binary image classification that uses a single instance per class to evolve a classifier. The method uses local binary patterns (LBP) as an image descriptor, support vector machine (SVM) as a classifier, and a one-way analysis of variance (ANOVA) as an analyser. Furthermore, a multi-objective fitness function is designed to detect distinct and informative regions of the images, and measure the goodness of the wrapped classifiers. The performance of the proposed method has been evaluated on six data sets and compared to the performances of both GP based (Two-tier GP and conventional GP) and non-GP (Naive Bayes, Support Vector Machines and hybrid Naive Bayes/Decision Trees) methods. The results show that a comparable or significantly better performance has been achieved by the proposed method over all methods on all of the data sets considered.",
• notes = "also known as \cite{6727019}",

}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

Citations