Tag-based web photo retrieval improved by batch mode re-tagging

Chen, L; Xu, D; Tsang, IW; Luo, J

Tag-based web photo retrieval improved by batch mode re-tagging

Chen, L Xu, D

Tsang, IW

Luo, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3440 - 3446
Issue Date:: 2010-08-31

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Field	Value	Language
dc.contributor.author	Chen, L	en_US
dc.contributor.author	Xu, D https://orcid.org/0000-0003-2775-9730	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Luo, J	en_US
dc.date.issued	2010-08-31	en_US
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3440 - 3446	en_US
dc.identifier.isbn	9781424469840	en_US
dc.identifier.issn	1063-6919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29433
dc.description.abstract	Web photos in social media sharing websites such as Flickr are generally accompanied by rich but noisy textual descriptions (tags, captions, categories, etc.). In this paper, we proposed a tag-based photo retrieval framework to improve the retrieval performance for Flickr photos by employing a novel batch mode re-tagging method. The proposed batch mode re-tagging method can automatically refine noisy tags of a group of Flickr photos uploaded by the same user within a short period by leveraging millions of training web images and their associated rich textual descriptions. Specifically, for one group of Flickr photos, we construct a group-specific lexicon which contains only the tags of all photos within the group. For each query tag, we employ the inverted file method to automatically find loosely labeled training web images. We propose a SVM with Augmented Features, referred to as AFSVM, to learn adapted classifiers to refine the annotation tags of photos by lever-aging the existing SVM classifiers of popular tags, which are associated with a large amount of positive training web images. Moreover, to further refine the annotation tags of photos in the same group, we additionally introduce an objective function that utilizes the visual similarities of photos within the group as well as the semantic proximities of their tags. Based on the refined tags, photos can be retrieved according to more reliable relevance scores. Extensive experiments demonstrate the effectiveness of our framework. ©2010 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/CVPR.2010.5539988	en_US
dc.title	Tag-based web photo retrieval improved by batch mode re-tagging	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Francisco, CA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Web photos in social media sharing websites such as Flickr are generally accompanied by rich but noisy textual descriptions (tags, captions, categories, etc.). In this paper, we proposed a tag-based photo retrieval framework to improve the retrieval performance for Flickr photos by employing a novel batch mode re-tagging method. The proposed batch mode re-tagging method can automatically refine noisy tags of a group of Flickr photos uploaded by the same user within a short period by leveraging millions of training web images and their associated rich textual descriptions. Specifically, for one group of Flickr photos, we construct a group-specific lexicon which contains only the tags of all photos within the group. For each query tag, we employ the inverted file method to automatically find loosely labeled training web images. We propose a SVM with Augmented Features, referred to as AFSVM, to learn adapted classifiers to refine the annotation tags of photos by lever-aging the existing SVM classifiers of popular tags, which are associated with a large amount of positive training web images. Moreover, to further refine the annotation tags of photos in the same group, we additionally introduce an objective function that utilizes the visual similarities of photos within the group as well as the semantic proximities of their tags. Based on the refined tags, photos can be retrieved according to more reliable relevance scores. Extensive experiments demonstrate the effectiveness of our framework. ©2010 IEEE.

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http://hdl.handle.net/10453/29433