Share Email Print
cover

Proceedings Paper

Generalized fuzzy k-means algorithms and their application in image compression
Author(s): Nicolaos B. Karayiannis
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This paper presents the development of generalized fuzzy k-means algorithms and their application in image compression based on vector quantization. The development of generalized fuzzy k-means algorithms is based on the search for partitions of the feature vector space other than those generated by existing fuzzy k-means algorithms. These alternative partitions can be obtained by relaxing one of the conditions imposed on the membership functions. The clustering problem is formulated as a constrained minimization problem, whose solution depends on the selection of a constrain function that satisfies certain conditions. The solution of this minimization problem results in a broad family of generalized fuzzy k-means algorithms, which include the existing fuzzy k-means algorithms as a special case. Moreover, the proposed formulation results in the minimum fuzzy k-means algorithms, which are computationally less demanding than the existing fuzzy k-means algorithms. A broad family of admissible constrain functions result in an extended family of fuzzy k-means algorithms, which at the limit provide the fuzzy k-means and minimum fuzzy k-means algorithms. The resulting algorithms are used in image compression based on vector quantization.

Paper Details

Date Published: 13 June 1995
PDF: 12 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211803
Show Author Affiliations
Nicolaos B. Karayiannis, Univ. of Houston (United States)


Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

© SPIE. Terms of Use
Back to Top