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Image Ranking

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Fundamentals of Image Data Mining

Part of the book series: Texts in Computer Science ((TCS))

Abstract

This chapter deals with image ranking , a technique to determine the similarity between images in the database so that only relevant images are retrieved. A number of important and commonly used similarity measures are discussed including the Lp distance, mass-based distance , cosine distance , χ2 statistics , HI, quadratic distance , and Mahalanobis distance . The pros and cons of each of the distances are highlighted. The next part of the chapter covers performance measures such as RPP , F-measure , PWH , PSR , and Bullseye. The pros, cons, and relationships between them are also explained in details. Readers are shown how similarity measures and performance measures work together to retrieve images correctly.

All that glitters is not gold.

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References

  1. Zhang D, Lu G (2003) Evaluation of similarity measurement for image retrieval. In: Proceedings of neural networks and signal processing

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  2. Aryal S et al (2017) Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. In: Knowledge and information systems, pp 1–28

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  3. Krumhansl C (1978) Concerning the applicability of geometric models to similarity data: the interrelationship between similarity and spatial density

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  4. Shojanazeri H, Teng S, Aryal S, Zhang D, Lu G (2018) A novel perceptual dissimilarity measure for image retrieval. In: Proceedings of IVCNZ2018

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Author information

Authors and Affiliations

  1. Federation University Australia, Churchill, VIC, Australia

    Dengsheng Zhang

Authors
  1. Dengsheng Zhang
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Correspondence to Dengsheng Zhang .

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© 2019 Springer Nature Switzerland AG

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Cite this chapter

Zhang, D. (2019). Image Ranking. In: Fundamentals of Image Data Mining. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-17989-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-17989-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17988-5

  • Online ISBN: 978-3-030-17989-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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