Looking beyond region boundaries: a robust image similarity measure using fuzzified region features

The performance of most region-based image retrieval systems depend critically on the accuracy of object segmentation. We propose a region matching approach, unified feature matching (UFM), which greatly increases the robustness of the retrieval system against segmentation related uncertainties. In our retrieval system, an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature reflecting color, texture, and shape properties. The resemblance between two images is then defined as the overall similarity between two families of fuzzy features, and quantified by the UFM measure. The system has been tested on a database of about 60,000 general-purpose images. Experimental results demonstrate improved accuracy and robustness.