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13 June 1995 Generalized fuzzy k-means algorithms and their application in image compression
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicolaos B. Karayiannis "Generalized fuzzy k-means algorithms and their application in image compression", Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); https://doi.org/10.1117/12.211803
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