NDLI: Binary image classification using genetic programming based on local binary patterns

Content Provider	IEEE Xplore Digital Library
Author	Al-Sahaf, H. Mengjie Zhang Johnston, M.
Copyright Year	2013
Description	Author affiliation: Evolutionary Comput. Res. Group, Victoria Univ. of Wellington, Wellington, New Zealand (Al-Sahaf, H.; Mengjie Zhang; Johnston, M.)
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 (Naïve Bayes, Support Vector Machines and hybrid Naïve 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.
Sponsorship	IEEE New Zealand Central Sect.
Starting Page	220
Ending Page	225
File Size	653869
Page Count	6
File Format	PDF
ISSN	21512191
e-ISBN	9781479908837
DOI	10.1109/IVCNZ.2013.6727019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-27
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Histograms Accuracy Feature extraction Vectors Analysis of variance
Content Type	Text
Resource Type	Article

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