31 May 2011 Unsupervised tattoo segmentation combining bottom-up and top-down cues
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Tattoo segmentation is challenging due to the complexity and large variance in tattoo structures. We have developed a segmentation algorithm for finding tattoos in an image. Our basic idea is split-merge: split each tattoo image into clusters through a bottom-up process, learn to merge the clusters containing skin and then distinguish tattoo from the other skin via top-down prior in the image itself. Tattoo segmentation with unknown number of clusters is transferred to a figureground segmentation. We have applied our segmentation algorithm on a tattoo dataset and the results have shown that our tattoo segmentation system is efficient and suitable for further tattoo classification and retrieval purpose.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Josef D. Allen, Josef D. Allen, Nan Zhao, Nan Zhao, Jiangbo Yuan, Jiangbo Yuan, Xiuwen Liu, Xiuwen Liu, } "Unsupervised tattoo segmentation combining bottom-up and top-down cues", Proc. SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 80630L (31 May 2011); doi: 10.1117/12.884368; https://doi.org/10.1117/12.884368


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