Query expansion by spatial co-occurrence for image retrieval

Li, Y; Geng, B; Zha, ZJ; Tao, D; Xu, C

Query expansion by spatial co-occurrence for image retrieval

Li, Y Geng, B Zha, ZJ Tao, D

Xu, C

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Publication Type:: Conference Proceeding
Citation:: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1177 - 1180
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Geng, B	en_US
dc.contributor.author	Zha, ZJ	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Xu, C	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1177 - 1180	en_US
dc.identifier.isbn	9781450306164	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31441
dc.description.abstract	The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively. Copyright 2011 ACM.	en_US
dc.relation.ispartof	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops	en_US
dc.relation.isbasedon	10.1145/2072298.2071968	en_US
dc.title	Query expansion by spatial co-occurrence for image retrieval	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Scottsdale, Arizona, USA
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The well-known bag-of-features (BoF) model is widely utilized for large scale image retrieval. However, BoF model lacks the spatial information of visual words, which is informative for local features to build up meaningful visual patches. To compensate for the spatial information loss, in this paper, we propose a novel query expansion method called Spatial Co-occurrence Query Expansion (SCQE), by utilizing the spatial co-occurrence information of visual words mined from the database images to boost the retrieval performance. In offline phase, for each visual word in the vocabulary, we treat the visual words that are frequently co-occurred with it in the database images as neighbors, base on which a spatial co-occurrence graph is built. In online phase, a query image can be expanded with some spatial co-occurred but unseen visual words according to the spatial co-occurrence graph, and the retrieval performance can be improved by expanding these visual words appropriately. Experimental results demonstrate that, SCQE achieves promising improvements over the typical BoF baseline on two datasets comprising 5K and 505K images respectively. Copyright 2011 ACM.

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http://hdl.handle.net/10453/31441