Bi-Level Classification of Color Indexed Image Histograms for Content Based Image Retrieval

This dissertation proposes content based image classification and retrieval with Classification and Regression Tree (CART). A simple CBIR system (WH) is designed and proved to be efficient even in the presence of distorted and noisy images. WH exhibits good performance in terms of precision, without using any intensive image processing feature extraction techniques. Unique indexed color histogram and wavelet decomposition based horizontal, vertical and diagonal image attributes have been chosen as the primary attributes in the design of the retrieval system. The output feature vectors of the WH method serve as input to the proposed decision tree based image classification and retrieval system. The performance of the proposed content based image classification and retrieval system is evaluated with the standard SIMPLIcity dataset which has been used in several previous works. The performance of the system is measured with precision as the metric. Holdout validation and k-fold cross validation are used to validate the results. The proposed system performs obviously better than SIMPLIcity and all the other compared methods.