Skip to main content

Distance and Similarity Measures

  • Reference work entry
Encyclopedia of Multimedia

Definition:The notion of distance or similarity between two color vectors is of paramount importance for the development of the vector processing techniques such as noise removal filters, edge detectors and image zoomers.

Since each color vector is uniquely determined via its magnitude (length) and direction (orientation), the evaluation of the color vectors can be realized in the magnitude domain, the directional domain, or it can utilize both vectors’ magnitude and direction [1], [2], [3].

The most commonly used measure to quantify the distance between two color vectors χ i =[x i1,x i2,x i3] and χ j =[x j1,x j2,x j3], in the magnitude domain, is the generalized weighted Minkowski metric:

where c is the non-negative scaling parameter denoting the measure of the overall discrimination power and the exponent L, with L=1 for the city-block distance, L=2 for the Euclidean distance and L→∞ for the chess-board distance, defines the nature of the distance metric [1], [2], [3]. The parameter...

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

Access this chapter

Institutional subscriptions

References

  1. R. Lukac, B. Smolka, K. Martin, K.-N. Plataniotis, and A.-N. Venetsanopulos, “Vector Filtering for Color Imaging,” IEEE Signal Processing Magazine, Vol. 22, No. 1, January 2005, pp. 74–86.

    Article  Google Scholar 

  2. K.-N. Plataniotis and A.-N. Venetsanopoulos, Color Image Processing and Applications, Springer Verlag, Berlin, 2000.

    Google Scholar 

  3. K.-N. Plataniotis, D. Androutsos, and A.-N. Venetsanopoulos, “Adaptive Fuzzy Systems for Multichannel Signal Processing,” Proceedings of the IEEE, Vol. 87, No. 9, September 1999, pp. 1601–1622.

    Article  Google Scholar 

Download references

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Science+Business Media, Inc.

About this entry

Cite this entry

(2006). Distance and Similarity Measures. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/0-387-30038-4_63

Download citation

Publish with us

Policies and ethics