A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency 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. The technique has binarized the significant bit planes of an image by selecting local thresholds. The 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 classification performance. 1. Introduction Incessant expansion of image datasets in terms of dimension and complexity has escalated the requirement to design techniques for efficient feature extraction. Selection of image features has been the basis for content based image classification 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 Thepade et al. in [2]. The process was followed by binarization of significant bit planes for feature vector extraction. Binarization process calculated the threshold value to differentiate the object of interest from its background. The novel method has been compared quantitatively with the techniques proposed by Thepade 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？inen [6], and Otsu [7]. Mean square error (MSE) method was followed for classification 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. Threshold selection has been essential to facilitate binarization of image to differentiate 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 affected 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 different
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