Coloring image search with coupled multi-index

Zheng, L; Wang, S; Tian, Q

Coloring image search with coupled multi-index

Zheng, L

Wang, S Tian, Q

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Publication Type:: Conference Proceeding
Citation:: 2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, 2015, pp. 137 - 141
Issue Date:: 2015-08-31

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Field	Value	Language
dc.contributor.author	Zheng, L https://orcid.org/0000-0002-1464-9500	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Tian, Q	en_US
dc.date.issued	2015-08-31	en_US
dc.identifier.citation	2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings, 2015, pp. 137 - 141	en_US
dc.identifier.isbn	9781479919482	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118085
dc.description.abstract	© 2015 IEEE. The precision of visual matching and the trade-off between accuracy and time efficiency have long been bottlenecks of image search systems. This work addresses the two problem simultaneously by introducing the coupled Multi-Index (cMI) structure. First, by combining SIFT and color features on the indexing-level, the discriminative power of visual words is greatly enhanced. Second, by reducing the number of inverted entries to be traversed, c-MI brings about significant improvement in time efficiency. Experiments are performed on two widely used benchmark datasets. We demonstrate both state-of-The-Art image search accuracy and cut-by-half query time.	en_US
dc.relation.ispartof	2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ChinaSIP.2015.7230378	en_US
dc.title	Coloring image search with coupled multi-index	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	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/Faculty of Engineering and Information Technology/School of Software
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:

© 2015 IEEE. The precision of visual matching and the trade-off between accuracy and time efficiency have long been bottlenecks of image search systems. This work addresses the two problem simultaneously by introducing the coupled Multi-Index (cMI) structure. First, by combining SIFT and color features on the indexing-level, the discriminative power of visual words is greatly enhanced. Second, by reducing the number of inverted entries to be traversed, c-MI brings about significant improvement in time efficiency. Experiments are performed on two widely used benchmark datasets. We demonstrate both state-of-The-Art image search accuracy and cut-by-half query time.

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http://hdl.handle.net/10453/118085