Abstract:
This paper proposes a fuzzy logic approach, UFM (unied 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 dened as the overall similaritybetween two families of fuzzy features, and quantied by a similarity measure, UFM measure, whichintegrates 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 quantication. 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 classication, similarity measure, fuzzied region
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