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Proceedings Paper

Unsupervised tattoo segmentation combining bottom-up and top-down cues
Author(s): Josef D. Allen; Nan Zhao; Jiangbo Yuan; Xiuwen Liu
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Paper Abstract

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.

Paper Details

Date Published: 31 May 2011
PDF: 9 pages
Proc. SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 80630L (31 May 2011); doi: 10.1117/12.884368
Show Author Affiliations
Josef D. Allen, Oak Ridge National Lab. (United States)
Florida State Univ. (United States)
Nan Zhao, Florida State Univ. (United States)
Jiangbo Yuan, Florida State Univ. (United States)
Xiuwen Liu, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 8063:
Mobile Multimedia/Image Processing, Security, and Applications 2011
Sos S. Agaian; Sabah A. Jassim; Yingzi Du, Editor(s)

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