Coupled binary embedding for large-scale image retrieval

Zheng, L; Wang, S; Tian, Q

Coupled binary embedding for large-scale image retrieval

Zheng, L

Wang, S Tian, Q

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2014, 23 (8), pp. 3368 - 3380
Issue Date:: 2014-01-01

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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	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2014, 23 (8), pp. 3368 - 3380	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118037
dc.description.abstract	Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts. © 1992-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2014.2330763	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Photography	en_US
dc.subject.mesh	Image Enhancement	en_US
dc.subject.mesh	Subtraction Technique	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Information Storage and Retrieval	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Coupled binary embedding for large-scale image retrieval	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	23	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	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.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts. © 1992-2012 IEEE.

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http://hdl.handle.net/10453/118037