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Issue No.09 - September (2002 vol.24)
pp: 1252-1267
ABSTRACT
<p><b>Abstract</b>—This paper proposes a fuzzy logic approach, UFM (unified 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) reflecting 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 defined as the overall similarity between two families of fuzzy features and quantified 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 influence of inaccurate segmentation and provides a very intuitive quantification. 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.</p>
INDEX TERMS
Content-based image retrieval, image classification, similarity measure, fuzzified region features, fuzzy data analysis.
CITATION
Yixin Chen, James Z. Wang, "A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 9, pp. 1252-1267, September 2002, doi:10.1109/TPAMI.2002.1033216
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