A Robust CBIR Approach Using Local Color Histograms

Global color histograms are well-known as a simple and often way to perform color-based image retrieval. However, it lacks spatial information about the image colors. The use of a grid of cells superimposed on the images and the use of local color histograms for each such cell improves retrieval in the sense that some notion of color location is taken into account. In such an approach however, retrieval becomes sensitive to image rotation and translation. In this thesis we present a new way to model image similarity, also using colors and a superimposing grid, via bipartite graphs. As a result, the technique is able to take advantage of color location but is not sensitive to rotation and translation. Experimental results have shown the approach to be very effective. If one uses global color histograms as a filter then our approach, named Harbin, becomes quite effcient as well (i.e., it imposes very little overhead over the use of global color histograms).