Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98460
Title: A unified learning framework for auto face annotation by mining web facial images
Authors: Wang, Dayong
Hoi, Steven Chu Hong
He, Ying
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
http://hdl.handle.net/10220/12274
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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