Nothing Special   »   [go: up one dir, main page]

Skip to main content

Geographic Image Retrieval Using Interest Point Descriptors

  • Conference paper
Advances in Visual Computing (ISVC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4842))

Included in the following conference series:

Abstract

We investigate image retrieval using interest point descriptors. New geographic information systems such as Google Earth and Microsoft Virtual Earth are providing increased access to remote sensed imagery. Content-based access to this data would support a much richer interaction than is currently possible. Interest point descriptors have proven surprisingly effective for a range of computer vision problems. We investigate their application to performing similarity retrieval in a ground-truth dataset manually constructed from 1-m IKONOS satellite imagery. We compare results of using quantized versus full descriptors, Euclidean versus Mahalanobis distance measures, and methods for comparing the sets of descriptors associated with query and target images.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Newsam, S., Yang, Y.: Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery. In: ACM International Symposium on Advances in Geographic Information Systems (ACM GIS) (2007)

    Google Scholar 

  2. Ashley, J., Flickner, M., Hafner, J., Lee, D., Niblack, W., Petkovic, D.: The query by image content (QBIC) system. In: ACM SIGMOD International Conference on Management of Data (1995)

    Google Scholar 

  3. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. In: Penn State University Technical Report CSE 06-009 (2006)

    Google Scholar 

  4. Bretschneider, T., Cavet, R., Kao, O.: Retrieval of remotely sensed imagery using spectral information content. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 2253–2255 (2002)

    Google Scholar 

  5. Bretschneider, T., Kao, O.: A retrieval system for remotely sensed imagery. In: International Conference on Imaging Science, Systems, and Technology, vol. 2, pp. 439–445 (2002)

    Google Scholar 

  6. Ma, A., Sethi, I.K.: Local shape association based retrieval of infrared satellite images. In: IEEE International Symposium on Multimedia (2005)

    Google Scholar 

  7. Li, Y., Bretschneider, T.: Semantics-based satellite image retrieval using low-level features. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, vol. 7, pp. 4406–4409 (2004)

    Google Scholar 

  8. Hongyu, Y., Bicheng, L., Wen, C.: Remote sensing imagery retrieval based-on Gabor texture feature classification. In: International Conference on Signal Processing, pp. 733–736 (2004)

    Google Scholar 

  9. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)

    Article  Google Scholar 

  10. Newsam, S., Wang, L., Bhagavathy, S., Manjunath, B.S.: Using texture to analyze and manage large collections of remote sensed image and video data. Journal of Applied Optics: Information Processing 43, 210–217 (2004)

    Google Scholar 

  11. Newsam, S., Kamath, C.: Retrieval using texture features in high resolution multi-spectral satellite imagery. In: SPIE Defense and Security Symposium, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI (2004)

    Google Scholar 

  12. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision 65, 43–72 (2005)

    Article  Google Scholar 

  13. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)

    Article  Google Scholar 

  14. Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477 (2003)

    Google Scholar 

  15. Schmid, C., Mohr, R.: Local grayvalue invariants for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 530–535 (1997)

    Article  Google Scholar 

  16. Wang, J., Zha, H., Cipolla, R.: Combining interest points and edges for content-based image retrieval. In: IEEE International Conference on Image Processing, pp. 1256–1259 (2005)

    Google Scholar 

  17. Wolf, C., Kropatsch, W., Bischof, H., Jolion, J.M.: Content based image retrieval using interest points and texture features. International Conference on Pattern Recognition 4, 4234 (2000)

    Google Scholar 

  18. Ledwich, L., Williams, S.: Reduced SIFT features for image retrieval and indoor localisation. In: Australasian Conference on Robotics and Automation (2004)

    Google Scholar 

  19. Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  20. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

  21. Kuhn, H.W.: The Hungarian Method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)

    Article  MathSciNet  Google Scholar 

  22. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)

    Google Scholar 

  23. Manjunath, B.S., Salembier, P., Sikora, T. (eds.): Introduction to MPEG7: Multimedia Content Description Interface. John Wiley & Sons, Chichester (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

George Bebis Richard Boyle Bahram Parvin Darko Koracin Nikos Paragios Syeda-Mahmood Tanveer Tao Ju Zicheng Liu Sabine Coquillart Carolina Cruz-Neira Torsten Müller Tom Malzbender

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Newsam, S., Yang, Y. (2007). Geographic Image Retrieval Using Interest Point Descriptors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76856-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76855-5

  • Online ISBN: 978-3-540-76856-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics