Publication Type

Journal Article

Version

Postprint

Publication Date

1-2014

Abstract

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.

Keywords

Face annotation, content-based image retrieval, machine learning, label refinement, web facial images, weak label

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

26

Issue

1

First Page

166

Last Page

179

ISSN

1041-4347

Identifier

10.1109/TKDE.2012.240

Publisher

IEEE

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1109/TKDE.2012.240

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