A Face Annotation Framework with Partial Clustering and Interactive Labeling

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.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Yuandong Tian, Wei Liu, Rong Xiao, Fang Wen, Xiaoou Tang
• Keywords: Computer Vision, CVPR 2007, Efficient Labeling Procedure, Face Annotation Framework, Face Annotation Technology, Face Annotation Workload, Later User Interaction