Novel feature extraction technique for content-based image recognition with query classification
Rik Kamal Kumar Das, Sudeep D. Thepade, Saurav Ghosh · International Journal of Computational Vision and Robotics · 2017
A surge of concern has been witnessed to manage the growing size of image information available from various sources namely internet and digital image capturing devices. The rich content of information available with image data has proved to be useful for analytical decision-making process. Content-based image recognition has been considered as an effective measure to identify the object of interest. The success of aforementioned procedure has largely been influenced by the method of feature extraction from the image content. Image binarisation has proved to be an efficient tool for feature vector extraction using various threshold selection techniques. The authors have proposed a novel feature extraction technique based on local threshold selection and have evaluated the technique on 17,021 images for performance assessment. The precision results for classification and retrieval have shown an increment of 17% and 13.1% respectively when compared to state-of-the-art techniques. A statistical test has also been conducted to establish the significance of the proposed method over the existing techniques.