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Exploring statistical correlations for image retrieval

Authors:

Xing LiAuthors Info & Claims

Multimedia Systems, Volume 11, Issue 4

Pages 340 - 351

https://doi.org/10.1007/s00530-006-0013-5

Published: 01 April 2006 Publication History

Abstract

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.

References

[1]

Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: Support vector machines for region-based image retrieval. In: Proceedings of the IEEE International Conference on Multimedia and Expo. Baltimore, Maryland (2003)

[2]

Jing, F., Li, M.J., Zhang, H.J., Zhang, B.: An efficient and effective region-based image retrieval framework. IEEE Trans. Image Process. 13(5):699---709 (2004)

Digital Library

[3]

Barnard, K., Duygulu, P., Forsyth, D.: Clustering art. Computer Vision and Pattern Recognition, II:434---439 (2001)

[4]

Barnard, K., Duygulu, P., Forsyth, D., Freitas, N., Blei, D.M., Jordan, M.: Matching words and pictures. J. Mach. Learn. Res. 3, 1107---1135 (2003)

Digital Library

[5]

Ma, W.Y., Manjunath, B.S.: Netra: a toolbox for navigating large image databases. In: Proceedings of the International Conference on Image Processing. Washington DC, USA (1997)

[6]

Wood, M.E.J., Campbell, N.W., Thomas, B.T.: Iterative refinement by relevance feedback in content-based digital image retrieval. In: Proceedings of the ACM International Conference on Multimedia. Bristol, UK (1998)

[7]

Zhu, L., Rao, A.B., Zhang, A.D.: Advanced feature extraction for keyblock-based image retrieval. Inform. Syst. 27(8), 537---557 (2002)

Digital Library

[8]

Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proceedings of the ACM International Conference on Multimedia. Ontario, Canada (2001)

[9]

Zhou, X.S., Huang, T.S.: Unifying keywords and visual contents in image retrieval. IEEE Multimedia 9(2), 23---33 (2002)

Digital Library

[10]

Chang, E., Goh, K., Sychay, G., Wu, G.: CBSA: Content-based soft annotation for multimodal image retrieval using Bayes point machines. In: Proceedings of the IEEE Transactions on CSVT Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, vol. 13, no. 1, pp. 26---38 (2003)

[11]

Zhang, H.J., Su, Z.: Improving CBIR by semantic propagation and cross-mode query expansion. Multi-Media Content Based Indexing and Retrieval (2001)

[12]

Porkaewand, K., Mehrotra, S.: Query reformulation for content based multimedia retrieval in MARS. Technical Report TR-MARS-99-05, University of California at Irvine (1999)

[13]

Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing, In: Proceedings of the ACM International Conference on Multimedia. Berkeley, CA USA (2003)

[14]

Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the 26th Annual International ACM SIGIR Conference. Toronto, Canada (2003)

[15]

Srihari, R.K.: Use of multimedia input in automated image annotation and content-based retrieval. Storage and Retrieval for Image and Video Databases, pp. 249---260 (1995).

[16]

Fellbaum, C.: WordNet: An Electronical Lexical Database. MIT Press, Cambridge, Mass (1998)

[17]

Cai, D., Yu, S., Wen, J.R. Ma, W.-Y.: VIPS: a vision-based page segmentation algorithm. Microsoft Technical Report, MSR-TR-2003-79 (2003)

[18]

Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 800---810 (2001)

Digital Library

[19]

Wang, X.J., Ma, W.Y., Li, X.: Data-driven approach for bridging the cognitive gap in image retrieval. In: Proceedings of the IEEE International Conference on Multimedia and Expo. Taipei, Taiwan (2004)

[20]

Sneath, P., Sokal, R.: Numerical Taxonomy: The Principles and Practice of Numerical Classification. W.H. Freeman, San Francisco, pp. 573 (1973)

[21]

Rubner, Y., Guibas, L.J., Tomasi, C.: The Earth mover's distance, multi-dimensional scaling, and color-based image retrieval. In: Proceedings of the ARPA Image Understanding Workshop, pp. 661---668. New Orleans, LA (1997)

[22]

Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 1(60):63---86 (2004)

[23]

Kadir, T.: Scale, saliency and scene description. Ph.D. Thesis, Oxford University (2002)

Cited By

Ntalianis KTsapatsoulis NDoulamis AMatsatsinis N(2014)Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolutionMultimedia Tools and Applications10.1007/s11042-012-0995-269:2(397-421)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s11042-012-0995-2
Lindner AShaji ABonnier NSüsstrunk SBabaguchi NAizawa KSmith JSatoh SPlagemann THua XYan R(2012)Joint statistical analysis of images and keywords with applications in semantic image enhancementProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2393417(489-498)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2393347.2393417
Ntalianis KDoulamis ATsapatsoulis NDoulamis AGonzàlez JBertini Mvan Gool LMatsatsinis NMoeslund TNixon MWang L(2010)Implicit visual concept modeling in image / video annotationProceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams10.1145/1877868.1877878(33-38)Online publication date: 29-Oct-2010
https://dl.acm.org/doi/10.1145/1877868.1877878
Show More Cited By

Exploring statistical correlations for image retrieval
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image Multimedia Systems

Multimedia Systems Volume 11, Issue 4

April 2006

87 pages

ISSN:0942-4962

Issue’s Table of Contents

Copyright © Copyright © 2006 Springer-Verlag.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 April 2006

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Cited By

Ntalianis KTsapatsoulis NDoulamis AMatsatsinis N(2014)Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolutionMultimedia Tools and Applications10.1007/s11042-012-0995-269:2(397-421)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s11042-012-0995-2
Lindner AShaji ABonnier NSüsstrunk SBabaguchi NAizawa KSmith JSatoh SPlagemann THua XYan R(2012)Joint statistical analysis of images and keywords with applications in semantic image enhancementProceedings of the 20th ACM international conference on Multimedia10.1145/2393347.2393417(489-498)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2393347.2393417
Ntalianis KDoulamis ATsapatsoulis NDoulamis AGonzàlez JBertini Mvan Gool LMatsatsinis NMoeslund TNixon MWang L(2010)Implicit visual concept modeling in image / video annotationProceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams10.1145/1877868.1877878(33-38)Online publication date: 29-Oct-2010
https://dl.acm.org/doi/10.1145/1877868.1877878
Rahman MBhattacharya PKompatsiaris YMarchand-Maillet SAvrithis YConnor NGatica-Perez DChua T(2009)Image retrieval with automatic query expansion based on local analysis in a semantical concept feature spaceProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1646396.1646422(1-8)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1646396.1646422
Tsikrika TDiou Cde Vries ADelopoulos AKompatsiaris YMarchand-Maillet SAvrithis YConnor NGatica-Perez DChua T(2009)Image annotation using clickthrough dataProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1646396.1646415(1-8)Online publication date: 8-Jul-2009
https://dl.acm.org/doi/10.1145/1646396.1646415
Liu DHua KYu N(2009)Efficiently support concurrent queries in multiuser CBIR systemsMultimedia Tools and Applications10.1007/s11042-008-0244-x42:3(273-293)Online publication date: 1-May-2009
https://dl.acm.org/doi/10.1007/s11042-008-0244-x
Rahman MAntani SLong RDemner-Fushman DThoma G(2009)Multi-modal query expansion based on local analysis for medical image retrievalProceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support10.1007/978-3-642-11769-5_11(110-119)Online publication date: 20-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-11769-5_11

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