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Query expansion enhancement by fast binary matching

Authors:

Xia Li,

Wengang Zhou,

Jinhui Tang,

Qi TianAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 877 - 880

https://doi.org/10.1145/2393347.2396336

Published: 29 October 2012 Publication History

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Abstract

Query expansion has been successfully employed to improve the performance of image retrieval system. It usually expands the original query based on the information from top ranked images. However, it may fail when some of the top ranked images are false positive or contain noisy features. To minimize the amount of irrelevant local features introduced, we propose to enhance query expansion by fast binary matching. More specifically, the noisy points on a candidate image are filtered out by local verification with their mapped locations on the query image. We further rank the expansion results by three different measurements based on local patch similarity in the image space. Experiments on partial-duplicate Web image search with a database of one million images show that the proposed approach achieves promising improvement in mean Average Precision (mAP) over the state-of-the-art query expansion approaches, and remains efficient in search time.

References

[1]

D. Lowe. Distinctive image features from scale-invariant key points. IJCV, 60(2):91--110, 2004.

Digital Library

Google Scholar

[2]

D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In Proc. CVPR, pages 2161--2168, 2006.

Digital Library

Google Scholar

[3]

H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In Proc. ECCV, 2008.

Digital Library

Google Scholar

[4]

O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman. Total recall: Automatic query expansion with a generative feature model for object retrieval. In Proc. ICCV, 2007.

Crossref

Google Scholar

[5]

J. Philbin, O. Chum, M. Isard, J. Sivic and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In Proc. CVPR, 2007.

Crossref

Google Scholar

[6]

S. Zhang, Q. Tian, G. Hua, Q. Huang, and S. Li, Descriptive Visual Words and Visual Phrases for Image Applications. In Proc. ACM Multimedia, 2009.

Digital Library

Google Scholar

[7]

W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian. Spatial coding for large scale partial-duplicate Web image search. In Proc. ACM Multimedia, 2010.

Digital Library

Google Scholar

[8]

O. Chum, A. Mikulik, M. Perdoch, and J. Matas. Total recall II: Query expansion revisited. In Proc. CVPR, 2011.

Digital Library

Google Scholar

[9]

Y. Kuo, K-T Chen, C-H Chiang, W.H. Hsu, Query expansion for hash-based image object retrieval. In Proc. ACM Multimedia, 2009.

Digital Library

Google Scholar

[10]

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, BREIEF: Binary robust independent elementary features. In Proc. ECCV, 2010.

Digital Library

Google Scholar

[11]

J. Xiao, Y. Fu, Y. Lu, and Q. Tian, Refining Image Retrieval Using One-Class Classification. In Proc. ICME, 2009.

Digital Library

Google Scholar

[12]

W. Zhou, H. Li, Y. Lu, Q. Tian, Large scale image search with geometric coding. In Proc. ACM Multimedia, 2011.

Digital Library

Google Scholar

[13]

J. Tang, S. Yan, R. Hong, G.-J. Qi, T.-S. Chua, Inferring Semantic Concepts from Community-Contributed Images and Noisy Tags. In Proc. ACM Multimedia, 2009.

Digital Library

Google Scholar

Cited By

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Wang LWang H(2017)Improving feature matching strategies for efficient image retrievalImage Communication10.1016/j.image.2017.02.00653:C(86-94)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.image.2017.02.006
Tolias GJégou H(2014)Visual query expansion with or without geometry: Refining local descriptors by feature aggregationPattern Recognition10.1016/j.patcog.2014.04.00747:10(3466-3476)Online publication date: Oct-2014
https://doi.org/10.1016/j.patcog.2014.04.007
Xiao JZhou WLi XWang MTian QBabaguchi NAizawa KSmith JSatoh SPlagemann THua XYan R(2012)Image tag re-ranking by coupled probability transitionProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2396328(849-852)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2393347.2396328

Index Terms

Query expansion enhancement by fast binary matching
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Wang LWang H(2017)Improving feature matching strategies for efficient image retrievalImage Communication10.1016/j.image.2017.02.00653:C(86-94)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.image.2017.02.006
Tolias GJégou H(2014)Visual query expansion with or without geometry: Refining local descriptors by feature aggregationPattern Recognition10.1016/j.patcog.2014.04.00747:10(3466-3476)Online publication date: Oct-2014
https://doi.org/10.1016/j.patcog.2014.04.007
Xiao JZhou WLi XWang MTian QBabaguchi NAizawa KSmith JSatoh SPlagemann THua XYan R(2012)Image tag re-ranking by coupled probability transitionProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2396328(849-852)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2393347.2396328

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An expansion and reranking approach for annotation-based image retrieval from Web

A framework of query expansion for image retrieval based on knowledge base and concept similarity

Query expansion based on term distribution and DBpedia features