Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers

Content based image classification is a vital component of machine learning and is attaining increasing importance in the field of image processing. This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification. A new block truncation coding (BTC) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification. The performances of classifier algorithms are compared in Receiver Operating Characteristic (ROC) Space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier are being used to observe the degree of classification for various techniques under six different feature vector extraction environments.