Skip to main content
SpringerLink
Log in

Signature-Based Indexing for Retrieval by Spatial Content in Large 2D-String Image Databases

  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1932))

Included in the following conference series:

  • 691 Accesses

Abstract

Image matching and content-based spatial similarity assessment based on the 2D-String image representation has been extensively studied. However, for large image databases, matching a query against every 2D-String has prohibitive cost. Indexing techniques are used to filter irrelevant images so that image matching algorithms can only focus on relevant ones. Current 2D-String indexing techniques are not efficient for handling large image databases. In this paper, the Two Signature Multi-Level Signature File (2SMLSF) is used as an efficient tree structure that encodes image information into two types of binary signatures. The 2SMLSF significantly reduces the storage requirements, responds to more types of queries, and its performance significantly improves over current techniques. For a simulated image databases of 131,072 images, a storage reduction of up to 35% and a querying performance improvement of up to 93% were achieved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. GUDIVADA, V. N., “qR-String: A Geometry-Based Representation for Efficient and Effective Retrieval of Images by Spatial Similarity,” Technical Report CS-95-02, School of Electrical Engineering and Computer Science, Ohio University, 1995.

    Google Scholar 

  2. GUDIVADA, V. N. and RAGHAVAN, V., “Design and Evaluation of Algorithms for Image Retrieval by Image Similarity,” ACM Trans. on Info. Sys. 13, April 1995, 115–144.

    Article  Google Scholar 

  3. EL-KWAE, E. and KABUKA, M., “A Robust Framework for Content-Based Retrieval by Spatial Similarity in Image Databases,” ACM Trans. on Info. Sys. 17(2), April 1999, 174–198.

    Article  Google Scholar 

  4. GUDIVADA, V. N. and JUNG, G. S., “An Algorithm for Content-Based Retrieval in Multimedia Databases,” Proc. of the Int’l Conf. on MM Comp. and Sys., Japan, June 17–23, 1995, 90–97.

    Google Scholar 

  5. Jane Hunter, “MPEG-7 Behind the Scenes,” D-Lib Magazine, 5(9), Sep. 1999.

    Google Scholar 

  6. CHANG, S. K., SHI, Q. Y., and YAN, C. W., “Iconic Indexing by 2-D Strings,” IEEE Transactions on Pattern Analysis and Machine Intelligence 9(3), May 1987, 413–428.

    Article  Google Scholar 

  7. CHANG, S. K. and JUNGERT, E.,“Pictorial Data Management Based Upon the Theory of Symbolic Projections,” Journal of visual Languages and Computing 2(3), Sep. 1991, 195–215.

    Google Scholar 

  8. LEE, S. Y. and HSU, S. Y., “Spatial Reasoning and Similarity Retrieval of Images Using 2D-C String Knowledge Representation,” Pattern Recognition 25(3), 1992, 305–318.

    Article  MathSciNet  Google Scholar 

  9. HUANG, P. W. and JEAN, Y. R., “Using 2D C+-Strings As Spatial Knowledge Representation For Image Database Systems,” Pattern Recognition 27(9), 1994, 1249–1257.

    Article  Google Scholar 

  10. EL-KWAE, E. and KABUKA, M., “A Boolean Neural Network Approach for Image Understanding,” Proc. of Artificial Neural Network in Engineering Con. (ANNIE’96), St Louis, Missouri, Nov. 10–13, 1996, 437–442.

    Google Scholar 

  11. C. Tsai, B. S. Manjunath, and R. Jagadeesan, “Automated Segmentation of Brain MR Images,” Pattern Recognition 28(12), 1995, 1825–1837.

    Article  Google Scholar 

  12. CHANG, C. C. and LEE, S., “Retrieval of Similar Pictures on Pictorial Databases,” Pattern Recognition 24(7), 1991, 675–680.

    Article  Google Scholar 

  13. PETRAKIS, E. and ORPHANOUDAKIS, s., “A Generalized Approach for Image Indexing and Retrieval Based on 2-D Strings,” First Workshop on Spatial Reasoning, Norway, Aug. 1993.

    Google Scholar 

  14. LEE, D. L., KIM, Y. M. and PATEL, G., “Efficient Signature File Methods for Text Retrieval,” IEEE Transactions on Knowledge and Data Engineering 7(3), Jun. 1995, 423–435.

    Article  Google Scholar 

  15. Faloutsos, C., “Access Methods for Text,” ACM Computing Surveys 17, 1985, 49–74.

    Article  Google Scholar 

  16. FALOUTSOS, C. and CHRISTODOULAKIS, S., “Signature Files: An Access Method for Documents and Its Analytical Performance Evaluation,” ACM Trans. on Ofc. Info. sys. 4, Oct. 1984, 267–288.

    Article  Google Scholar 

  17. ROBERTS, C. S., “Partial-Match Retrieval via Method of Superimposed Coding,” Proceedings of IEEE 67(12), Dec. 1979, 1624–1642.

    Article  Google Scholar 

  18. DAVIS, R. S. and K. RAMAMOHANARAO, K,. A, “Two-Level Superimposed Coding Scheme for Partial Match Retrieval,” Information Systems 8(4), 1983, 273–280.

    Article  Google Scholar 

  19. EL-KWAE, E. and KABUKA, M., “Efficient Content-Based Indexing of Large Image Databases,” ACM Transactions on Information Systems, (to appear).

    Google Scholar 

  20. LEE S. Y. and SHAN, M. K., “Access Methods of Image Databases,” International Journal of Pattern Recognition and Artificial Intelligence 4, 1990, 27–44.

    Article  Google Scholar 

  21. LEE, S.Y., SHAN, M. K. YANG, W., “Similarity Retrieval of Iconic Image Databases,” Pattern Recognition 22(6), 1989, 675–682.

    Article  Google Scholar 

  22. TSENG, J., HWANG, T. and YANG, W., “Efficient Image Retrieval Algorithms for Large Spatial Databases,” Int’l Journal of Pattern Recognition and Art. Int. 8(4), 1994, 919–944.

    Article  Google Scholar 

  23. DEPPISCH, U., “S-Tree: A dynamic balanced signature index for office retrieval,” Proc. of the ACM Conf. on Research and Development in Info. Ret., Pisa, Italy, Sep. 1986, 77–87.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, NC 28223

    El-Kwae A. Essam

Authors
  1. El-Kwae A. Essam
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, 01-237, Warsaw, Poland

    Zbigniew W. Raś

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Department of Science and Technology, Waseda University, 61-414, 3-4-1, Ohkubo, Shinjuku-ku, 169-8555, Tokyo, Japan

    Setsuo Ohsuga

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Essam, EK.A. (2000). Signature-Based Indexing for Retrieval by Spatial Content in Large 2D-String Image Databases. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_11

Download citation

  • DOI: https://doi.org/10.1007/3-540-39963-1_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41094-2

  • Online ISBN: 978-3-540-39963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation