Supervised Machine Learning of KFCG Algorithm and MBTC features for efficient classification of Image Database and CBIR Systems

A Content Based Image Retrieval System (CBIR) is proposed using features representing the images quite uniquely. Two algorithms have been used to extract two types of features from each of the images stored in the database. They are Modified Block Truncation Coding (MBTC) and Keri's Fast Codebook Generation (KFCG) algorithm. The features obtained from the images using the two algorithms represent each of them with unique and different values. KFCG algorithm forms a codebook containing codebook vectors. Codebook vectors are a set of code words used to encode the images. KFCG algorithm requires lesser time for vector quantization process. MBTC features can only represent images and hence useful for retrieval of images from database. MBTC features consist of upper and lower mean values of three colour components of colour space of pixels in the colour images. These features yield high performance for the CBIR system proposed in this paper. The images are analysed, compared and retrieved from the database using one of the two types of features. The analysis and comparison is also done using two different methods. Euclidean Distance is used to identify the image being enquired using two different types of features. Similarly, Support Vector Machine (SVM) is also used to obtain the result using two different types of features. SVM is implemented in two stages. First, the features are used to train the SVM variables. During training, some of the image features of different categories being utilized for training purpose are marked by users according to their categories. In the next stage, the SVM variables obtained during training are used to classify the images. A database of 1000 images of 10 different categories are used to perform assessment of the system. The system is implemented using MATLAB.