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
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