A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval

This paper proposes a fuzzy logic approach, UFM (uni ed feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature (fuzzy set) re ecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image, and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then de ned as the overall similarity between two families of fuzzy features, and quanti ed by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the in uence of inaccurate segmentation, and provides a very intuitive quanti cation. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images. Index Terms| Content-based image retrieval, image classi cation, similarity measure, fuzzi ed region features, fuzzy data analysis.