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A Face Annotation Framework with Partial Clustering and Interactive Labeling

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5 Author(s)
Yuandong Tian ; Shanghai Jiaotong Univ., Shanghai ; Wei Liu ; Rong Xiao ; Fang Wen
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Face annotation technology is important for a photo management system. In this paper, we propose a novel interactive face annotation framework combining unsupervised and interactive learning. There are two main contributions in our framework. In the unsupervised stage, a partial clustering algorithm is proposed to find the most evident clusters instead of grouping all instances into clusters, which leads to a good initial labeling for later user interaction. In the interactive stage, an efficient labeling procedure based on minimization of both global system uncertainty and estimated number of user operations is proposed to reduce user interaction as much as possible. Experimental results show that the proposed annotation framework can significantly reduce the face annotation workload and is superior to existing solutions in the literature.

Published in:

Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on

Date of Conference:

17-22 June 2007