Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Large-scale tattoo image retrieval

: Manger, Daniel

Postprint urn:nbn:de:0011-n-2194177 (1012 KByte PDF)
MD5 Fingerprint: 4497760840eb713bae592af96b15c5ac
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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)
Conference on Computer and Robot Vision (CRV) <9, 2012, Toronto>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
content-based image retrieval; biometrics; tattoo images; identification; forensic database

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.