Research Article A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification Sudeep Thepade, 1 Rik Das, 2 and Saurav Ghosh 3 1 Pimpri Chinchwad College of Engineering, Akurdi, Sector 26, Pradhikaran, Nigdi, Pune, Maharashtra 411033, India 2 Xavier Institute of Social Service, Dr. Camil Bulcke Path (Purulia Road), P.O. Box 7, Ranchi, Jharkhand 834001, India 3 A.K. Choudhury School of Information Technology, University of Calcutta, 92 APC Road, Kolkata, West Bengal 700009, India Correspondence should be addressed to Rik Das; rikdas78@gmail.com Received 8 July 2014; Revised 20 October 2014; Accepted 21 October 2014; Published 18 November 2014 Academic Editor: Jie Zhou Copyright © 2014 Sudeep Tepade et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A number of techniques have been proposed earlier for feature extraction using image binarization. Efciency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. Te technique has binarized the signifcant bit planes of an image by selecting local thresholds. Te proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classifcation performance. 1. Introduction Incessant expansion of image datasets in terms of dimension and complexity has escalated the requirement to design techniques for efcient feature extraction. Selection of image features has been the basis for content based image clas- sifcation as reviewed by Andreopoulos and Tsotsos in [1]. In this work, a new feature extraction technique applying binarization on bit planes using local threshold technique has been proposed. A digital image can be separated into bit planes to understand the importance of each bit in the image as shown by Tepade et al. in [2]. Te process was followed by binarization of signifcant bit planes for feature vector extraction. Binarization process calculated the threshold value to diferentiate the object of interest from its background. Te novel method has been compared quantita- tively with the techniques proposed by Tepade et al. in [2] and by Kekre et al. in [3] and four other widely used image binarization techniques proposed by Niblack [4], Bernsen [5], Sauvola and Pietik¨ ainen [6], and Otsu [7]. Mean square error (MSE) method was followed for classifcation performance evaluation of the proposed technique with respect to the existing techniques for feature vector extraction. 2. Related Work Various methods have been used for feature extraction that has implemented image binarization as a tool to denote the object of interest and its background, respectively. Treshold selection has been essential to facilitate binarization of image to diferentiate the object from its background. Valizadeh et al. [8], Chang et al. [9], and Gatos et al. [10] have described that threshold selection has been afected by a number of factors including ambient illumination, variance of gray levels within the object and the background, and inadequate contrast. Process of threshold selection has been categorized into three diferent techniques, namely, mean threshold selection, local threshold selection, and global threshold selection. Existing methods of feature extraction from images using selection of mean threshold were adopted by Tepade et al. in [2] and by Kekre et al. in [3]. Te frst method of feature extraction using even and odd images [2] Hindawi Publishing Corporation Journal of Engineering Volume 2014, Article ID 439218, 13 pages http://dx.doi.org/10.1155/2014/439218