Mining weakly labeled web facial images for search-based face annotation

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Dayong, He, Ying, Zhu, Jianke, Hoi, Steven C. H.
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2014
Subjects:
Online Access:https://hdl.handle.net/10356/102521
http://hdl.handle.net/10220/18955
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-102521
record_format dspace
spelling sg-ntu-dr.10356-1025212020-05-28T07:18:12Z Mining weakly labeled web facial images for search-based face annotation Wang, Dayong He, Ying Zhu, Jianke Hoi, Steven C. H. School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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. Accepted version 2014-03-24T01:10:46Z 2019-12-06T20:56:21Z 2014-03-24T01:10:46Z 2019-12-06T20:56:21Z 2014 2014 Journal Article Wang, D., Hoi, S. C. H., He, Y., & Zhu, J. (2014). Mining weakly labeled web facial images for search-based face annotation. IEEE Transactions on Knowledge and Data Engineering, 26(1), 166-179. 1041-4347 https://hdl.handle.net/10356/102521 http://hdl.handle.net/10220/18955 10.1109/TKDE.2012.240 en IEEE transactions on knowledge and data engineering © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version of this article is available at [DOI: http://dx.doi.org/10.1109/TKDE.2012.240]. 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. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Wang, Dayong
He, Ying
Zhu, Jianke
Hoi, Steven C. H.
Mining weakly labeled web facial images for search-based face annotation
description 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.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Wang, Dayong
He, Ying
Zhu, Jianke
Hoi, Steven C. H.
format Article
author Wang, Dayong
He, Ying
Zhu, Jianke
Hoi, Steven C. H.
author_sort Wang, Dayong
title Mining weakly labeled web facial images for search-based face annotation
title_short Mining weakly labeled web facial images for search-based face annotation
title_full Mining weakly labeled web facial images for search-based face annotation
title_fullStr Mining weakly labeled web facial images for search-based face annotation
title_full_unstemmed Mining weakly labeled web facial images for search-based face annotation
title_sort mining weakly labeled web facial images for search-based face annotation
publishDate 2014
url https://hdl.handle.net/10356/102521
http://hdl.handle.net/10220/18955
_version_ 1681059010788196352