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Image classification and querying using composite region templates
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Matching and retrieval based on the vocabulary and grammar of color patterns
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Object-based image similarity computation using inductive learning of contour-segment relations
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EdgeFlow: a technique for boundary detection and image segmentation
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Texture classification and segmentation using wavelet frames
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Learning-based linguistic indexing of pictures with 2--d MHMMs
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Photographic Authentication through Untrusted Terminals
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Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
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Addressing CBIR efficiency, effectiveness, and retrieval subjectivity simultaneously
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Image Database Design Based on 9D-SPA Representation for Spatial Relations
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Enhanced Perceptual Distance Functions and Indexing for Image Replica Recognition
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MILES: Multiple-Instance Learning via Embedded Instance Selection
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Sparse Multiscale Patches for Image Processing
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Using Hilbert scan on statistical color space partitioning
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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.