Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2011

Abstract

In this paper, we investigate a search-based face annotation framework by mining weakly labeled facial images that are freely available on the internet. A key component of such a search-based annotation paradigm is to build a database of facial images with accurate labels. This is however challenging since facial images on the WWW are often noisy and incomplete. To improve the label quality of raw web facial images, we propose an effective Unsupervised Label Refinement (ULR) approach for refining the labels of web facial images by exploring machine learning techniques. We develop effective optimization algorithms to solve the large-scale learning tasks efficiently, and conduct an extensive empirical study on a web facial image database with 400 persons and 40,000 web facial images. Encouraging results showed that the proposed ULR technique can significantly boost the performance of the promising search-based face annotation scheme.

Keywords

Auto face annotation, Web facial images, Unsupervised learning

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

SIGIR '11: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval: July 24-28, Beijing

First Page

535

Last Page

544

ISBN

9781450309349

Identifier

10.1145/2009916.2009989

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/2009916.2009989

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