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|Title:||A unified learning framework for auto face annotation by mining web facial images||Authors:||Wang, Dayong
Hoi, Steven Chu Hong
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Wang, D., Hoi, S. C. H., & He, Y. (2012). A unified learning framework for auto face annotation by mining web facial images. Proceedings of the 21st ACM international conference on Information and knowledge management.||Abstract:||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.||URI:||https://hdl.handle.net/10356/98460
|DOI:||http://dx.doi.org/10.1145/2396761.2398444||Rights:||© 2012 ACM.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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