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Content-based image retrieval: An application to tattoo images

Content-based image retrieval: An application to tattoo images,10.1109/ICIP.2009.5414140,Anil K. Jain,Jung-Eun Lee,Rong Jin,Nicholas Gregg

Content-based image retrieval: An application to tattoo images   (Citations: 5)
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Tattoo images on human body have been routinely collected and used in law enforcement for suspect and victim identification. However, the current practice of matching tattoos is based on keywords. Assigning keywords to individual tattoo images is both tedious and subjective. We have developed a content-based image retrieval system for a tattoo image database. The system automatically extracts image features based on the Scale Invariant Feature Transform (SIFT). Side information, i.e., body location of tattoos and tattoo classes, is utilized to improve the retrieval time and retrieval accuracy. Geometrical constraints are also introduced in SIFT keypoint matching to reduce false retrievals. Experimental results on 1,000 queries against an operational database of 63,593 tattoo images show a rank-20 accuracy of 94.2%; the average matching time per query is 2.9 sec. on Intel Core 2, 2.66 GHz, 3 GB RAM processor.
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    • ...Similar applications are implemented in [9], [10], [11]...

    B. Szantoet al. Sketch4match — Content-based image retrieval system using sketches

    • ...Point Pattern Matching(PPM)is a commonly encountered problem in a wide range of disciplines, including image registration [1], image classification [2], image retrieval [3], stereo vision [4], etc...

    Jian Zhaoet al. Inexact point pattern matching algorithm based on Relative Shape Conte...

    • ...Although CBIR is inherently a difficult problem due to the gap between low-level image features and high-level semantics [2], CBIR techniques have been effective for near-duplicate image detection problems [3], [4], [5]...
    • ...To improve the accuracy of keypoint matching, geometric constraints are used to reduce false matchings [3]...
    • ...system whose goal is to find tattoo images in the database that are near-duplicates of the query tattoo image (see Figure 2). Although our goal is near-duplicate detection, tattoo image retrieval is substantially more challenging than other application domains because of the large variation in the visual appearance of the same tattoo [3], [7]...

    Jung-Eun Leeet al. Unsupervised Ensemble Ranking: Application to Large-Scale Image Retrie...

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