Novel Technique in Block Truncation Coding Based Feature Extraction for Content Based Image Identification

Feature vector extraction has been the key component to define the success rate for content based image recognition. Block truncation coding is a simple technique which has facilitated various methods for effective feature vector extraction for content based image recognition. A new technique named Sorted Block Truncation Coding (SBTC) has been introduced in this work. Three different public datasets namely Wang Dataset, Oliva and Torralba (OT-Scene) Dataset and Caltech Dataset consisting of 6,221 images on the whole was considered for evaluation purpose. The technique has stimulated superior performance in image recognition when compared to classification and retrieval results with other existing techniques of feature extraction. The technique was also evaluated in lossy compression domain for the test images. Various parameters like precision, recall, misclassification rate and F1 score has been considered to evaluate the performances. Statistical evaluations have been carried out for all the comparisons by introducing paired t test to establish the significance of the findings. Classification and retrieval with proposed technique has shown a minimum of 14.4 % rise in precision results compared to the existing state-of-the art techniques.