A novel information theoretic approach to wavelet feature selection for texture classification

Authors:
Imran Naseem;Duc-Son Pham;Svetha Venkatesh
Affiliations:
College of Engineering, Karachi Institute of Economics and Technology (KIET), Karachi 75190, Pakistan;Institute for Multisensor Processing and Content Analysis (IMPCA), Department of Computing, Curtin University of Technology Perth, WA 6102, Australia;Centre for Pattern Recognition and Data Analytics (PRaDA), Deakin University, GEELONG VIC 3220, Australia
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

In this research we address the problem of discriminant subband selection for texture classification. A novel Effective Information based Subband Selection (EISS) algorithm is proposed which utilizes the intra-class and inter-class distributions. Essentially these distributions are used to calculate the class-based entropy for a given subband. This class-based information is incorporated in the total information content of the training images to develop a robust Effective Information (EI) criterion. Only the subbands with the top EI criteria are allowed to participate in the classification process. The proposed EISS algorithm is evaluated on Brodatz texture database and has shown to outperform the most relevant method based on mutual information criterion.