Skip to main content

Query Point Movement Techniques for Content-Based Image Retrieval

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems
  • 5 Accesses

Definition

Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. To search for such an image, query point movement techniques iteratively move the query point closer to the target image for each round of the user’s relevance feedback until the target image is found. The goals of query point movement techniques include avoiding local maximum traps, achieving fast convergence, reducing computation overhead, and guaranteeing to find the target.

Historical Background

Images in a database are characterized by their visual features, and represented as points in a multidimensional feature space. A query point is one of these image points, selected to find similar images represented by image points nearest to the query point in the feature space. This cluster of nearby or relevant image points has a shape (see Figs. 1 and 2) referred to as the query shape.

Query Point...

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 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover 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

Recommended Reading

  1. Chakrabarti K, Ortega-Binderberger M, Mehrotra S, Porkaew K. Evaluating refined queries in top-k retrieval systems. IEEE Trans knowledge and Data Eng. 2004;16(2):256–70.

    Article  Google Scholar 

  2. Cox IJ, Miller ML, Minka TP, Papathomas TP, Yianilos PN. The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans Image Proces. 2000;9(1):20–37.

    Article  Google Scholar 

  3. Flickner M, Sawhney HS, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P. Query by image and video content: The QBIC system. IEEE Comput. 1995;28(9):23–32.

    Article  Google Scholar 

  4. Hua KA, Yu N, Liu D. Query decomposition: a multiple neighborhood approach to relevance feedback processing in contentbased image retrieval. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.

    Google Scholar 

  5. Ishikawa Y, Subramanya R, Faloutsos C. MindReader: querying databases through multiple examples. In: Proceedings of the 24th International Conference on Very Large Data Bases; 1998. p. 218–27.

    Google Scholar 

  6. Kim D-H, Chung C-W. Qcluster: relevance feedback using adaptive clustering for content-based image retrieval. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003. p. 599–610.

    Google Scholar 

  7. Liu D, Hua KA. Support concurrent queries in multiuser CBIR systems. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 1379–81.

    Google Scholar 

  8. Liu D, Hua KA, Vu K, Yu N. Fast query point movement techniques with relevance feedback for content-based image retrieval. In advances in database technology. In: Proceedings of the 10th International Conference on Extending Database Technology; 2006. p. 700–17.

    Google Scholar 

  9. Rui Y, Huang T, Ortega M, Mehrotra S. Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol. 1998;8(5):644–55.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kien A. Hua .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Hua, K.A., Liu, D. (2018). Query Point Movement Techniques for Content-Based Image Retrieval. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_295

Download citation

Publish with us

Policies and ethics