research-article

Image retrieval with automatic query expansion based on local analysis in a semantical concept feature space

Authors:

Md Mahmudur Rahman,

Prabir BhattacharyaAuthors Info & Claims

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

Article No.: 20, Pages 1 - 8

https://doi.org/10.1145/1646396.1646422

Published: 08 July 2009 Publication History

Abstract

We present an automatic query expansion approach by generalizing the vector space model of information retrieval. In this framework, the images are presented by vectors of weighted concepts similar to the keyword-based representation in the text retrieval domain. The concepts comprise of color and texture patches from local image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical model is built by utilizing a multi-class Support Vector Machine (SVM)-based classification technique. For automatic query expansion, the correlations between concepts are analyzed based on the neighborhood proximity between the concepts in encoded images by considering the local feedback information. The experimental results on a photographic image collection demonstrate the effectiveness of the proposed query expansion approaches.

References

[1]

R. B. Yates, and B. R. Neto, Modern Information Retrieval, 1st ed., Addison Wesley, 1999.

Digital Library

[2]

G. Salton, and M. J. McGill, Introduction to Modern Information Retrieval, Computer Science Series, McGraw-Hill, 1983.

Digital Library

[3]

J. Xu, and W. B. Croft, "Improving the effectiveness of information retrieval with local context analysis," ACM Trans. on Info. Sys., vol. 18, no., pp. 79--112, 2000.

Digital Library

[4]

J. Xu, and W. B. Croft, "Query Expansion Using Local and Global Document Analysis," Proc. 19th Annual Int'l ACM SIGIR Conf. on Research and Develop. in Info. Retrieval, pp. 4--11, 1996.

Digital Library

[5]

C. J. Crouch, "An approach to the automatic construction of global thesauri," Info. Process. and Management, vol. 26, no. 5, pp. 629--640, 1990.

Digital Library

[6]

J. J. Rocchio, "Relevance feedback in information retrieval," In the SMART retrieval system -experiments in automatic document processing,(G. Salton ed). pp. 313--323, Prentice Hall, Englewood Cliffs, NJ, 1971.

[7]

G. Salton, and C. Buckley, "Improving Retrieval Performance by Relevance Feedback," J. American Soc. for Info. Scie., vol. 41, no. 4, pp. 288--297, 1999.

[8]

I. Ruthven, and M. Lalmas, "A survey on the use of relevance feedback for information access systems," Knowledge Eng. Review, vol. 18, no. 2, pp. 95--145, 2003.

Digital Library

[9]

A. Smeulder, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-Based Image Retrieval at the End of the Early Years," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 12, pp. 1349--1380, 2000.

Digital Library

[10]

Y. Liua, D. Zhang, G. Lu, and W. Y. Ma, "A survey of content-based image retrieval with high-level semantics," Pattern Recognition, vol. 40, pp. 262--282, 2007.

Digital Library

[11]

R. Datta, D. Joshi, J. Li and J. Z. Wang, "Image retrieval: Ideas, influences, and trends of the new age," ACM Computing Surveys vol. 40 (2), pp. 1--60, 2008.

Digital Library

[12]

L. Zhu, A. Zhang, A. Rao, and R. Srihari, "Theory of keyblock-based image retrieval," ACM Trans. Info. Syst., vol. 20, no. 2, pp. 224--257, 2002.

Digital Library

[13]

E. Chang, G. Kingshy, G. Sychay, and W. Gang, "CBSA: Content-based Soft Annotation for Multimodal Image Retrieval Using Bayes Point Machines," IEEE Trans. Circuits and Syst. for Video Tech., vol. 13, pp. 26--38, 2003.

Digital Library

[14]

F. Jing, M. Li, H. J. Zhang, and B. Zhang, "An efficient and effective region-based image retrieval framework," IEEE Trans. on Image Process., vol. 13, pp. 699--709, 2004.

Digital Library

[15]

J. H. Lim, "Building visual vocabulary for image indexation and query formulation," Pattern Anal. Applications (Special Issue on Image Indexation), vol. 4, pp. 125--139, 2001.

[16]

J. Vogel, and B. Schiele, "Semantic Modeling of Natural Scenes for Content-Based Image Retrieval," Internat. J. Comput. Vis., vol. 72, no. 2, pp. 133--157, 2007.

Digital Library

[17]

X. S. Zhou, and T. S. Huang, "Unifying keywords and visual contents in image retrieval," IEEE Multimedia, vol. 4, no. 2, pp. 23--33, 2002.

Digital Library

[18]

X. J. Wang, W. Y. Ma, and X. Li, "Exploring Statistical Correlations for Image Retrieval," Multimedia Systems, vol. 11(4), pp. 340--351, 2006.

Digital Library

[19]

H. J. Zhang and Z. Su, "Improving CBIR by Semantic Propagation and Cross-Mode Query Expansion," Multi-Media Content Based Indexing and Retrieval, 2001.

[20]

J. Jeon, V. Lavrenko and R. Manmatha, "Automatic Image Annotation and Retrieval using Cross-Media Relevance Models," Proc. of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 119--126, 2003.

Digital Library

[21]

Y. Rui, T. S. Huang, "Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval", IEEE Circuits Syst. Video Technol., vol. 8, no. 5, pp. 644--655, 1999.

Digital Library

[22]

R. Attar, and A. S. Fraenkel, "Local feedback in full-text retrieval systems," J. ACM, vol. 24, no. 3, pp. 397--417, 1977.

Digital Library

[23]

O. Yasushi, M. Tetsuya, and K. Kiyohiko, "A fuzzy document retrieval system using the keyword connection matrix and a learning method," Fuzzy Sets and Systems, vol. 39, no. 2, pp. 163--179, 1991.

Digital Library

[24]

R. M. Haralick, Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man Cybernetics, vol. 3, pp. 610--21, 1973.

[25]

J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121--167, 1998.

Digital Library

[26]

T. F. Wu, C. J. Lin, and R. C. Weng, "Probability Estimates for Multi-class Classification by Pairwise Coupling," J. Machine Learn Res., vol. 5, pp. 975--1005, 2004.

Digital Library

[27]

C. W. Hsu, and C. J. Lin, "A comparison of methods for multi-class support vector machines," IEEE Trans. Neural Network, vol. 13, no. 2, pp. 415--425, 2002.

Digital Library

[28]

C. C. Chang, and C. J. Lin, "LIBSVM: a library for support vector machines," 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

[29]

M. Grubinger, P. Clough, H. Müller, and T. Deselears, "The IAPR-TC12 benchmark: A new evaluation resource for visual information systems," Proc. Int'l Workshop on Language Resources for Content-Based Image Retrieval, pp. 1323, 2006.

Cited By

Rahman MAntani SThoma G(2018)A query expansion framework in image retrieval domain based on local and global analysisInformation Processing and Management: an International Journal10.1016/j.ipm.2010.12.00147:5(676-691)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2010.12.001
Wu XNgo CZhu YPeng Q(2018)Boosting web video categorization with contextual information from social webWorld Wide Web10.1007/s11280-011-0129-115:2(197-212)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s11280-011-0129-1
Mai HKim M(2014)Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrievalMultimedia Tools and Applications10.1007/s11042-013-1360-972:1(331-360)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s11042-013-1360-9

Index Terms

Image retrieval with automatic query expansion based on local analysis in a semantical concept feature space
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Conferences

CIVR '09: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2009

383 pages

ISBN:9781605584805

DOI:10.1145/1646396

Conference Chairs:
Yiannis Kompatsiaris
CERTH-ITI, Greece
,
Stephane Marchand-Maillet
Univ. of Geneva, Switzerland
,
Program Chairs:
Yannis Avrithis
NTUA, Greece
,
Noel O Connor
DCU, Ireland
,
Daniel Gatica-Perez
Idiap Research Institute, Switzerland
,
Tat-Seng Chua
National University of Singapore, Singapore

Copyright © 2009 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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CIVR '09

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SIGMM

CIVR '09: CIVR '09 - International Conference on Image and Video Retrieval

July 8 - 10, 2009

Santorini, Fira, Greece

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Cited By

Rahman MAntani SThoma G(2018)A query expansion framework in image retrieval domain based on local and global analysisInformation Processing and Management: an International Journal10.1016/j.ipm.2010.12.00147:5(676-691)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2010.12.001
Wu XNgo CZhu YPeng Q(2018)Boosting web video categorization with contextual information from social webWorld Wide Web10.1007/s11280-011-0129-115:2(197-212)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s11280-011-0129-1
Mai HKim M(2014)Utilizing similarity relationships among existing data for high accuracy processing of content-based image retrievalMultimedia Tools and Applications10.1007/s11042-013-1360-972:1(331-360)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s11042-013-1360-9

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