Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2012

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

Keywords

face annotation, image retrieval, inductive learning, sparse coding, transductive learning, web facial images

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

CIKM'12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management: October 29 - November 2, Maui, Hawaii

First Page

1392

Last Page

1401

ISBN

9781450311564

Identifier

10.1145/2396761.2398444

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/2396761.2398444

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