A Unified Learning Framework for Auto Face Annotation by Mining Web Facial Images
Conference Proceeding Article
Auto face annotation plays an important role in many real-world multimedia information and knowledge management systems. Recently there is a surge of research interests in mining weakly-labeled facial images on the internet to tackle this long-standing research challenge in computer vision and image understanding. In this paper, we present a novel unified learning framework for face annotation by mining weakly labeled web facial images through interdisciplinary efforts of combining sparse feature representation, content-based image retrieval, transductive learning and inductive learning techniques. In particular, we first introduce a new search-based face annotation paradigm using transductive learning, and then propose an effective inductive learning scheme for training classification-based annotators from weakly labeled facial images, and finally unify both transductive and inductive learning approaches to maximize the learning efficacy. We conduct extensive experiments on a real-world web facial image database, in which encouraging results show that the proposed unified learning scheme outperforms the state-of-the-art approaches
Computer Sciences | Databases and Information Systems
Data Management and Analytics
CIKM'12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management: October 29 - November 2, 2012, Maui, Hawaii
City or Country
WANG, Dayong; HOI, Steven; and HE, Ying.
A Unified Learning Framework for Auto Face Annotation by Mining Web Facial Images. (2012). CIKM'12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management: October 29 - November 2, 2012, Maui, Hawaii. 1392-1401. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2345