Experiments on Image Classification and Retrieval using Statistics on Pixels Position

In this paper we have proposed a color indexing scheme for image classification and retrieval using color features. Experiments were made on the Corel 1000 database, for three different color spaces, LAB, HSV and RGB. In our tests, for image classification, two discriminative classifiers, k-NN (k-Nearest Neighborhood) and SVM (Support Vector Machine) were used. Two new distances were defined and used in k-NN experiments and the results were compared with results obtained using k-NN with three well known distances, Canberra, Euclidian and Manhattan. For image retrieval, the performance of the proposed method, measured in terms of average precision and average recall were compared with performance obtained with methods using color, texture and shape features. Our approach retrieves the highest number of relevant images compared to other more computationally expensive techniques. This color indexing method can improve the robustness of finding images with a similar color composition and can be used as a simple, fast and computationally simple filter, whose output can be then processed by other methods.