research-article

SymCity: feature selection by symmetry for large scale image retrieval

Authors:

Giorgos Tolias,

Yannis Kalantidis,

Yannis AvrithisAuthors Info & Claims

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

Pages 189 - 198

https://doi.org/10.1145/2393347.2393379

Published: 29 October 2012 Publication History

Abstract

Many problems, including feature selection, vocabulary learning, location and landmark recognition, structure from motion and 3d reconstruction, rely on a learning process that involves wide-baseline matching on multiple views of the same object or scene. In practical large scale image retrieval applications however, most images depict unique views where this idea does not apply. We exploit self-similarities, symmetries and repeating patterns to select features within a single image. We achieve the same performance compared to the full feature set with only a small fraction of its index size on a dataset of unique views of buildings or urban scenes, in the presence of one million distractors of similar nature. Our best solution is linear in the number of correspondences, with practical running times of just a few milliseconds.

References

[1]

S. Agarwal, N. Snavely, I. Simon, S. M. Seitz, and R. Szeliski. Building Rome in a day. In ICCV, 2009.

[2]

Y. Avrithis, Y. Kalantidis, G. Tolias, and E. Spyrou. Retrieving landmark and non-landmark images from community photo collections. In ACM Multimedia, 2010.

Digital Library

[3]

S. Bagon, O. Boiman, and M. Irani. What is a good image segment? A unified approach to segment extraction. In ECCV, 2008.

Digital Library

[4]

H. Bay, T. Tuytelaars, and L. Van Gool. SURF: Speeded up robust features. In ECCV, 2006.

Digital Library

[5]

O. Boiman, E. Shechtman, and M. Irani. In defense of nearest-neighbor based image classification. In CVPR, 2008.

[6]

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 ICCV, 2007.

[7]

H. Cornelius, M. Perdoch, J. Matas, and G. Loy. Efficient symmetry detection using local affine frames. In ECIA, 2007.

Digital Library

[8]

P. Doubek, J. Matas, M. Perdoch, and O. Chum. Image matching and retrieval by repetitive patterns. In ICPR, 2010.

Digital Library

[9]

S. Gammeter, L. Bossard, T. Quack, and L. V. Gool. I know what you did last summer: Object-level auto-annotation of holiday snaps. In ICCV, 2009.

[10]

E. Gavves, C. G. M. Snoek, and A. W. M. Smeulders. Visual synonyms for landmark image retrieval. CVIU, 2012.

Digital Library

[11]

H. Jegou, M. Douze, and C. Schmid. On the burstiness of visual elements. In CVPR, 2009.

[12]

H. Jegou, M. Douze, and C. Schmid. Improving bag-of-features for large scale image search. IJCV, 87(3):316--336, 2010.

Digital Library

[13]

H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. In CVPR, 2010.

[14]

Y. Keller and Y. Shkolnisky. An algebraic approach to symmetry detection. ICPR, pages 186--189, 2004.

Digital Library

[15]

J. Knopp, J. Sivic, and T. Pajdla. Avoiding confusing features in place recognition. In ECCV, 2010.

Digital Library

[16]

S. Lazebnik, C. Schmid, and J. Ponce. Semi-local affine parts for object recognition. In BMVC, 2004.

[17]

F. Li and J. Kosecka. Probabilistic location recognition using reduced feature set. In ICRA, 2006.

[18]

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

Digital Library

[19]

G. Loy and J.-O. Eklundh. Detecting symmetry and symmetric constellations of features. In ECCV, 2006.

Digital Library

[20]

A. Mikulik, M. Perdoch, O. Chum, and J. Matas. Learning a fine vocabulary. In ECCV, 2010.

Digital Library

[21]

M. Muja and D. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In ICCV, 2009.

[22]

N. Naikal, A. Yang, and S. Shankar Sastry. Informative feature selection for object recognition via sparse pca. In ICCV, 2011.

Digital Library

[23]

M. Perdoch, O. Chum, and J. Matas. Efficient representation of local geometry for large scale object retrieval. In CVPR, 2009.

[24]

F. Perronnin, Y. Liu, J. Sanchez, and H. Poirier. Large-scale image retrieval with compressed Fisher vectors. In CVPR, 2010.

[25]

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

[26]

G. Schindler, M. Brown, and R. Szeliski. City-scale location recognition. In CVPR, 2007.

[27]

G. Schindler, P. Krishnamurthy, R. Lublinerman, Y. Liu, and F. Dellaert. Detecting and matching repeated patterns for automatic geo-tagging in urban environments. In CVPR, 2008.

[28]

E. Shechtman and M. Irani. Matching local self-similarities across images and videos. In CVPR, 2007.

[29]

J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, pages 1470--1477, 2003.

Digital Library

[30]

C. Sun and D. Si. Fast reflectional symmetry detection using orientation histograms. Real Time Imaging, 5(1):63--74, 1999.

Digital Library

[31]

G. Tolias and Y. Avrithis. Speeded-up, relaxed spatial matching. In ICCV, 2011.

Digital Library

[32]

P. Turcot and D. Lowe. Better matching with fewer features: the selection of useful features in large database recognition problems. In ICCV, 2009.

[33]

T. Tuytelaars, A. Turina, and L. Van Gool. Noncombinatorial detection of regular repetitions under perspective skew. PAMI, 2003.

Digital Library

Cited By

Alzou’bi AGan K(2020)Discovering informative features in large-scale landmark image collectionJournal of Information Science10.1177/016555152095065348:2(237-250)Online publication date: 1-Sep-2020
https://doi.org/10.1177/0165551520950653
Siméoni OIscen ATolias GAvrithis YChum O(2019)Graph-based particular object discoveryMachine Vision and Applications10.1007/s00138-019-01005-z30:2(243-254)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s00138-019-01005-z
Simeoni OIscen ATolias GAvrithis YChum O(2018)Unsupervised Object Discovery for Instance Recognition2018 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2018.00194(1745-1754)Online publication date: Mar-2018
https://doi.org/10.1109/WACV.2018.00194
Show More Cited By

Index Terms

SymCity: feature selection by symmetry for large scale image retrieval
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image ACM Conferences

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

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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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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Citations

Cited By

Alzou’bi AGan K(2020)Discovering informative features in large-scale landmark image collectionJournal of Information Science10.1177/016555152095065348:2(237-250)Online publication date: 1-Sep-2020
https://doi.org/10.1177/0165551520950653
Siméoni OIscen ATolias GAvrithis YChum O(2019)Graph-based particular object discoveryMachine Vision and Applications10.1007/s00138-019-01005-z30:2(243-254)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s00138-019-01005-z
Simeoni OIscen ATolias GAvrithis YChum O(2018)Unsupervised Object Discovery for Instance Recognition2018 IEEE Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV.2018.00194(1745-1754)Online publication date: Mar-2018
https://doi.org/10.1109/WACV.2018.00194
Miaojing Shi Avrithis YJegou H(2015)Early burst detection for memory-efficient image retrieval2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2015.7298659(605-613)Online publication date: Jun-2015
https://doi.org/10.1109/CVPR.2015.7298659
Pang SXue JGao ZTian Q(2015)Image re-ranking with an alternating optimizationNeurocomputing10.1016/j.neucom.2015.03.040165:C(423-432)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.03.040
Cao YRitz CRaad R(2015)Feature selection for low bit rate mobile augmented reality applicationsImage Communication10.1016/j.image.2015.06.00836:C(115-126)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.image.2015.06.008
Zuo ZLuo YTao DXu CHua KRui YSteinmetz RHanjalic ANatsev AZhu W(2014)Multi-view Multi-task Feature Extraction for Web Image ClassificationProceedings of the 22nd ACM international conference on Multimedia10.1145/2647868.2655002(1137-1140)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2647868.2655002
Cao YRitz CRaad R(2014)Quality of experience-based image feature selection for mobile augmented reality applications2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS)10.1109/ICSPCS.2014.7021099(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/ICSPCS.2014.7021099
Cao YRitz CRaad R(2014)Adaptive and robust feature selection for low bitrate mobile augmented reality applications2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS)10.1109/ICSPCS.2014.7021084(1-7)Online publication date: Dec-2014
https://doi.org/10.1109/ICSPCS.2014.7021084
Tao RGavves ESnoek CSmeulders A(2014)Locality in Generic Instance Search from One ExampleProceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition10.1109/CVPR.2014.269(2099-2106)Online publication date: 23-Jun-2014
https://dl.acm.org/doi/10.1109/CVPR.2014.269
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