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Large-scale tattoo image retrieval

 
: Manger, Daniel

:
Postprint urn:nbn:de:0011-n-2194177 (1012 KByte PDF)
MD5 Fingerprint: 4497760840eb713bae592af96b15c5ac
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Erstellt am: 2.12.2014


IEEE Computer Society:
CRV 2012, Ninth Conference on Computer and Robot Vision. Proceedings : Toronto, Canada, 28-30 May 2012
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2012
ISBN: 978-0-7695-4683-4
ISBN: 978-1-4673-1271-4 (Print)
S.454-459
Conference on Computer and Robot Vision (CRV) <9, 2012, Toronto>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
content-based image retrieval; biometrics; tattoo images; identification; forensic database

Abstract
In current biometric-based identification systems, tattoos and other body modifications have shown to provide a useful source of information. Besides manual category label assignment, approaches utilizing state-of-the-art content-based image retrieval (CBIR) techniques have become increasingly popular. While local feature-based similarities of tattoo images achieve excellent retrieval accuracy, scalability to large image databases can be addressed with the popular bag-of-word model. In this paper, we show how recent advances in CBIR can be utilized to build up a large-scale tattoo image retrieval system. Compared to other systems, we chose a different approach to circumvent the loss of accuracy caused by the bag-of-word quantization. Its efficiency and effectiveness are shown in experiments with several tattoo databases of up to 330,000 images.

: http://publica.fraunhofer.de/dokumente/N-219417.html