Skip to main content

Sketch Case Based Spatial Topological Data Retrieval

  • Conference paper
Advances in Multimedia Information Processing - PCM 2006 (PCM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4261))

Included in the following conference series:

Abstract

A large proportion of the information can be regarded as spatial data which is spatial position related. For accessing spatial databases, different query specification techniques have been proposed. But traditional query methods are tedious and cannot realize fuzzy query. A content-based spatial data retrieval system is presented to afford users a sketch interface which has the ability to accept fuzzy retrieval. Firstly the retrieval algorithm builds the spatial topological vector by refining the 9-intersection model metrically. Then the independent topological relations are extracted by training ICA assisted fuzzy SVMs, which can remove redundancy among the binary relations and reduce the dimension in complex spatial scene. In query processing the tftimes idf model is referenced, and the similarity is calculated by cosine distance function on the weight vectors of the query scene and each of spatial scenes in database. The experimental results show the recall factor and precision factor are improved compared with the query method without ICA and SVM.

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

Access this chapter

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. Goodchild, M.: Integrating GIS and spatial data analysis: Problems and possibilities. International Journal of Geographical Information Systems 6(5), 407–423 (1992)

    Article  Google Scholar 

  2. Morehouse, S.: The ARC/INFO geographic information system. Computers and Geosciences: An international journal 18(4), 435–443 (1992)

    Article  Google Scholar 

  3. Ooi, B.C.: Efficient Query Processing in Geographic Information Systems. Springer, Heidelberg (1990)

    MATH  Google Scholar 

  4. Medeiros, C.B., Pires, F.: Databases for GIS. SIGMOD Record 23(1), 107–115 (1994)

    Article  Google Scholar 

  5. Egenhofer, M.J.: Spatial SQL: a query and presentation language. IEEE Transactions on Knowledge and Data Engieering 6(1), 86–95 (1994)

    Article  Google Scholar 

  6. Flickner, M., Sawhnewy, H., Niblack, W., et al.: Query by image and video Content: The QBIC System. IEEE Computer 28(9), 23–32 (1995)

    Google Scholar 

  7. Rosenthal, A., Heiler, S., Manola, F.: An example of knowledge-based query processing in a CAD/CAM DBMS. In: Proceedings of 10th International Conference on Very Large Data Bases, Singapore, pp. 363–370 (1984)

    Google Scholar 

  8. Egenhofer, M.J.: Query Processing in Spatial-Query-by-Sketch. Journal of Visual Languages and Computing 8(4), 403–424 (1997)

    Article  Google Scholar 

  9. Egenhofer, M.J., Sharma, J.: Topological relations between regions in R2 and Z2. In: Abel, D.J., Ooi, B.-C. (eds.) SSD 1993. LNCS, vol. 692, pp. 316–336. Springer, Heidelberg (1993)

    Google Scholar 

  10. Egenhofer, M.J.: On the Equivalence of Topological Relations. International Journal of Geographical Information Systems 8(6), 133–152 (1994)

    Google Scholar 

  11. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  12. Qi, Y., DeMenthon, D., Doermann, D.: Hybrid Independent Component Analysis and Support Vector Machine Learning Scheme for Face Detection. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2001), Salt Lake City,Utah (May 2001)

    Google Scholar 

  13. Cichocki, A., Amari, S., Yang, H.H.: A new learning algorithm for blind signal separation. In: NIPS 8, pp. 752–763. MIT Press, Cambridge (1996)

    Google Scholar 

  14. Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent component representations for face recognition. In: SPIE Conf. on Human Vision and Electronic Imaging III, vol. 3299, pp. 528–539 (1998)

    Google Scholar 

  15. Platt, J.C.: Probabilistic outputs for support vector machines for pattern recognition. In: Smola, A., Barlett, P., Scholkopf, B. (eds.) Advances in Large margin Classifiers, Kluwer Academic Publishers, Boston (1999)

    Google Scholar 

  16. Salton, G., Buckley, C.: Term Weighting approaches in automatic text retrieval. Information Processing and Management 24(5), 513–523 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhen-ming, Y., Liang, Z., Hong, P. (2006). Sketch Case Based Spatial Topological Data Retrieval. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_79

Download citation

  • DOI: https://doi.org/10.1007/11922162_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48766-1

  • Online ISBN: 978-3-540-48769-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics