A novel region-based multiple classifier color image retrieval system is presented. In our approach, a region-growing technique is first employed to cluster connected color pixels with the same color in an image to form color regions which are the primitive elements utilized in our proposed approach. Then, three complementary region-based classifiers that we developed are selected in the classifier selection stage, which include color classifier, shape classifier, and relational classifier. In each classifier, a virtue probability representing the probability that an image is similar to the query image is defined. Thereafter a set of virtue probabilities is calculated in each classifier. Next, the measurement dependent methods are applied to combine the virtue probabilities of classifiers in the decision combination stage. The dynamic selection scheme designed in the decision combination stage can further improve the system performance dramatically. Experimental results reveal the feasibility and validity of our proposed approach in solving the color image retrieval problem.

In this paper, we present a novel region-based color image retrieval system using geometric properties. A region-growing technique is firstly employed to cluster the connected color pixels with the same color in an image to form color regions. Then, two most important descriptive geometry features are extracted. One is the spatial relational graph (SRG), another is the Fourier description coefficients (FDC) of each color region. In the matching stage, the modified relational distance graph matching between two SRG is performed firstly to find the best matches with the minimum relational distance. Then, the shape matching is applied to obtain the best vertex match with the minimum geometric distance. Experimental results reveal the feasibility of our proposed approach in solving color image retrieval problem.