Skip to main content

Image Similarity

  • Reference work entry
  • First Online:
  • 173 Accesses

Synonyms

Image distance; Similarity measure; Visual similarity

Definition

Given a pair of images each described by a feature set, image similarity is defined by comparing the feature set on the basis of a similarity function. In a typical Visual Information Retrieval system, while searching for a query image among the elements of the data set of images, knowledge of the domain will be expressed by formulating a similarity measure between the query and data set based on some visual features. Therefore, measuring meaningful image similarity consists of two intrinsic elements: finding a set of features for adequately describing the image content and finding a suitable metric for assessing the similarity on the basis of feature space. The feature set can be computed globally for the entire image or locally for a small group of pixels such as regions or objects. The similarity measure can be different depending on the types of features. Typically, the feature space is assumed to be...

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

Buying options

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

Learn about institutional subscriptions

Recommended Reading

  1. Chen Y, Wang JZ. Image categorization by learning and reasoning with regions. J Machine Learn Res. 2004;5(2):913–39.

    MathSciNet  Google Scholar 

  2. Datta R, Joshi D, Li J, Wang JZ. Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv. 2008;40(65)

    Article  Google Scholar 

  3. Fei-Fei L, Perona P. A bayesian hierarchical model for learning natural scene categories. In: Proceedings of the IEEE International Conference on Computer Vision and Pattersn Recognition; 2005. p. 524–31.

    Google Scholar 

  4. Jing F, Li M, Zhang H-J, Zhang B. An efficient and effective region-based image retrieval framework. IEEE Trans Image Process. 2004;13(5):699–709.

    Article  Google Scholar 

  5. Long F, Zhang H-J, Feng DD. Fundamentals of content-based image retrieval. In: Feng DD, Siu WC, Zhang H-J, editors. Multimedia information retrieval and management – technological fundamentals and applications. Berlin/Heidelberg/New York: Springer; 2003.

    MATH  Google Scholar 

  6. Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.

    Article  MathSciNet  Google Scholar 

  7. Rubner Y, Tomasi C, Guibas LJ. The earth mover’s distance as a metric for image retrieval. Int J Comput Vis. 2000;40(2):99–121.

    Article  MATH  Google Scholar 

  8. Rui Y, Huang TS, Chang S-F. Image retrieval: current techniques, promising directions and open issues. J Vis Commun Image Represent. 1999;13(10):39–62.

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Santini S, Jain R. Similarity measures. IEEE Trans Pattern Anal Mach Intell. 1999;21(9):871–83.

    Article  Google Scholar 

  11. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell. 2000;22(12):1349–80.

    Article  Google Scholar 

  12. Wang B, Li Z, Li M, Ma W-Y. Large-scale duplicate detection for web image search. In: Proceedings of the IEEE International Conference on Multimedia and Expo; 2006. p. 353–6.

    Google Scholar 

  13. Zhou D, Bousquet O, Lal T, Weston J, Scholkopf B. Learning with local and global consistency. In: Proceedings of the Advances in Neural Information Processing System; 2003. p. 321–8.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao Mei .

Editor information

Editors and Affiliations

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

Mei, T., Rui, Y. (2018). Image Similarity. 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_1014

Download citation

Publish with us

Policies and ethics