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Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation
Jan. 2014 (vol. 26 no. 1)
pp. 166-179
Dayong Wang, Nanyang Technological University, Singapore
Steven C.H. Hoi, Nanyang Technological University, Singapore
Ying He, Nanyang Technological University, Singapore
Jianke Zhu, Zhejiang University, Hangzhou
This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques. We formulate the learning problem as a convex optimization and develop effective optimization algorithms to solve the large-scale learning task efficiently. To further speed up the proposed scheme, we also propose a clustering-based approximation algorithm which can improve the scalability considerably. We have conducted an extensive set of empirical studies on a large-scale web facial image testbed, in which encouraging results showed that the proposed ULR algorithms can significantly boost the performance of the promising SBFA scheme.
Index Terms:
Face,Optimization,Feature extraction,Noise measurement,Machine learning,Approximation algorithms,Humans,weak label,Face annotation,content-based image retrieval,machine learning,label refinement,web facial images
Citation:
Dayong Wang, Steven C.H. Hoi, Ying He, Jianke Zhu, "Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 166-179, Jan. 2014, doi:10.1109/TKDE.2012.240
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