Skip to main content
SpringerLink
Log in

An effective image retrieval mechanism using family-based spatial consistency filtration with object region

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of “TREC Video Retrieval Evaluation 2005 (TRECVID2005)”. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein, J. Malik. Blobworld: A system for region-based image indexing and retrieval. In Proceeding of the 3rd International Conference on Visual Information Systems, IEEE Computer Society, Amsterdam, Holand, vol. 2, pp. 509–516, 1999.

    Google Scholar 

  2. J. Sivic, A. Zisserman. Video google: A text retrieval approach to object matching in videos. In Proceedings of the 9th IEEE International Conference on Computer Vision, IEEE, Nice, France, vol. 2, pp. 1470–1477, 2003.

    Chapter  Google Scholar 

  3. J. Philbin, O. Chum, M. Isard, J. Sivic, A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, IEEE, Minneapolis, USA, pp. 1–8, 2007.

    Google Scholar 

  4. K. Gao, S. X. Lin, J. B. Guo, D. M. Zhang, Y. D. Zhang, Y. F. Wu. Object retrieval based on spatially frequent items with informative patches. In Proceedings of IEEE International Conference on Multimedia and Expo, IEEE, Hanoverian, Germany, pp. 1305–1308, 2008.

    Chapter  Google Scholar 

  5. S. Lazebnik, C. Schmid, J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proceedings of IEEE Computer Society Conference on Conference on Computer Vision and Pattern Recognition, IEEE, New York, USA, vol. 2, pp. 2169–2178, 2006.

    Google Scholar 

  6. Q. F. Zheng, W. Q. Wang, W. Gao. Effective and efficient object-based image retrieval using visual phrases. In Proceedings of the 14th Annual ACM International Conference on Multimedia, ACM, Santa Barbara, USA, pp. 77–80, 2006.

  7. D. Nistér, H. Stewénius. Scalable recognition with a vocabulary tree. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, New York, USA, vol. 2, pp. 2161–2168, 2006.

    Google Scholar 

  8. C. Schmid, R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530–535, 1997.

    Article  Google Scholar 

  9. T. Tuytelaars, L. Van Gool. Content-based image retrieval based on local affinely invariant regions. Lecture Notes in Computer Science, Springer, vol. 1614, pp. 493–500, 1999.

    Article  Google Scholar 

  10. F. Schaffalitzky. A. Zisserman. Multi-view matching for unordered image sets. In Proceedings of the 7th European Conference on Computer Vision, Lecture Notes in Computer Science, Springer, Copenhagen, Denmark, vol. 2350, pp. 414–431, 2002.

    Google Scholar 

  11. A. Baumberg. Reliable feature matching across widely separated views. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Hilton Head Island, USA, vol. 1, pp. 774–781, 2000.

    Google Scholar 

  12. K. Mikolajczyk, C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, vol. 60, no. 1, pp. 63–86, 2004.

    Article  Google Scholar 

  13. T. Lindeberg. Feature detection with automatic scale selection. International Journal of Computer Vision, vol. 30, no. 2, pp. 79–116, 1998.

    Article  Google Scholar 

  14. D. G. Lowe. Distinctive image features from scale invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.

    Article  Google Scholar 

  15. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool. A comparison of affine region detectors. International Journal of Computer Vision, vol. 65, no. 1–2, pp. 43–72, 2005.

    Article  Google Scholar 

  16. K. Yamamoto, R. Oi. Color correction for multi-view video using energy minimization of view networks. International Journal of Automation and Computing, vol. 5, no. 3, pp. 234–245, 2008.

    Article  Google Scholar 

  17. S. F. Liu, C. McMahon, M. Darlington, S. Culley, P. Wild. EDCMS: A content management system for engineering documents. International Journal of Automation and Computing, vol. 4, no. 1, pp. 56–70, 2007.

    Article  Google Scholar 

  18. R. Baeza-Yates, B. Ribeiro-Neto. Modern Information Retrieval, ACM Press, pp. 24–34, 1999.

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116023, PRC

    Jing Sun & Ying-Jie Xing

Authors
  1. Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying-Jie Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Sun.

Additional information

This work was supported by National High Technology Research and Development Program of China (863 Program) (No. 2007AA01Z416) and National Natural Science Foundation of China (No. 60773056), Beijing New Star Project on Science and Technology (No. 2007B071), Natural Science Foundation of Liaoning Province of China (No. 20052184)

Jing Sun received the B.A. degree in Yanshan University, PRC and the M.A. degree in Dalian University of Technology, PRC in 1997 and 2002, respectively. Since 2005, she has been a Ph.D. candidate in Dalian University of Technology. She is currently a lecturer at the School of Mechanical and Engineering of Dalian University of Technology.

Her research interests include feature extract, image matching, and object retrieval.

Ying-Jie Xing received the B.A. degree and the M. A. degree in Harbin Institute of Technology, PRC in 1983 and 1986, respectively. He received the Ph.D. degree from University of Yamanashi, Japan in 1996. He is currently an associate professor at the School of Mechanical and Engineering of Dalian University of Technology, PRC.

His research interests include feature extract, image processing, and pattern recognition.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, J., Xing, YJ. An effective image retrieval mechanism using family-based spatial consistency filtration with object region. Int. J. Autom. Comput. 7, 23–30 (2010). https://doi.org/10.1007/s11633-010-0023-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-010-0023-9

Keywords

Search

Navigation