Image Classification Using Advanced Block Truncation Coding with Ternary Image Maps

Incredible escalation of Information Technology leads to generation, storage and transfer of enormous information. Easy and round the clock access of data has been made possible by virtue of world wide web. The high capacity storage devices and communication links facilitates the archiving of information in the form of multimedia. This type of information comprises of images in majority and is growing in number by leaps and bounds. But the usefulness of this information will be at stake if maximum information is not retrieved in minimum time. The huge database of information comprising of multiple number of image data is diversified mix in nature. Proper Classification of Image data based on their content is highly applicable in these databases to form limited number of major categories. The novel ternary block truncation coding (Ternary BTC) is proposed in the paper, also the comparison of Binary block truncation coding (Binary BTC) and Ternary Block Truncation Coding is done for image classification. Here two image databases are considered for experimentation. The proposed ternary BTC is found to be better than Binary BTC for image classification as indicated by higher average success rate.