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Computer Vision, IEEE International Conference on (2007)
Rio de Janeiro, Brazil
Oct. 14, 2007 to Oct. 21, 2007
ISBN: 978-1-4244-1630-1
pp: 1-7
Jianxiong Xiao , The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. csxjx@cse.ust.hk, welleast@cse.ust.hk
Jingdong Wang , The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. welleast@cse.ust.hk
Ping Tan , The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. ptan@cse.ust.hk, welleast@cse.ust.hk
Long Quan , The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. quan@cse.ust.hk, welleast@cse.ust.hk
ABSTRACT
A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse affinity propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised affinity propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches.
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CITATION

P. Tan, J. Xiao, J. Wang and L. Quan, "Joint Affinity Propagation for Multiple View Segmentation," 2007 11th IEEE International Conference on Computer Vision(ICCV), Rio de Janeiro, 2007, pp. 1-7.
doi:10.1109/ICCV.2007.4408928
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