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A Novel Feature Extraction Technique with Binarization of Significant Bit Information

Sudeep Thepade, Rik Das, Saurav Ghosh


In this paper, a novel technique of feature extraction has been proposed in which the authors have attempted to capture the higher bit planes for binarization of image information. Extraction of features for classification of images into meaningful categories has evolved as a challenging and vital domain of research. Binarizaton of images based on various threshold selection methods has been used as an effective tool for feature extraction. A public dataset named Wang dataset comprising of diversified image categories was used to carry out the experimental process. The proposed technique was compared with state-of-the-art methods of feature extraction using traditional binarization techniques. The result for classification by feature extraction with the proposed technique has shown an accuracy of 93% and has outclassed all the compared methods.


Feature Extraction, Binarization, Local Threshold, Bit Plane Slicing, Classification

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