Content Based Image Classification with Thepade's Static and Dynamic Ternary Block Truncation Coding

Image data has been considered as a vital source of information with far-fetched growth of Information Technology. World Wide Web has facilitated easy and round the clock access of data. Archiving of image data in good proportion has been made possible with high capacity storage devices and communication links. Time and efficiency have been considered as most important factors for information recognition from these datasets. The huge numbers of information databases have diverse categories of image data. Limited number of major categories can be formed based on the contents of the images with the help of image classification. The authors have proposed two novel techniques of feature extraction in this work and have compared the same with the existing techniques of feature extraction for classification results. The proposed techniques have exhibited higher performance efficiency compared to the state-of-the art techniques and have principally contributed to boost up classification performance.