NDLI: A new FPCA-based fast segmentation method for color images

Content Provider	IEEE Xplore Digital Library
Author	Abodpour, A. Kasaei, S.
Copyright Year	2004
Description	Author affiliation: Math. Sci. Dept., Sharif Univ. of Technol., Tehran, Iran (Abodpour, A.)
Abstract	Fuzzy objective function-based clustering methods are proved to be fast tools for classification and segmentation purposes. Unfortunately, most of the available fuzzy clustering methods are using the spherical or ellipsoidal distances, which are proved to result in spurious clusters, when working on color data. In this paper, a general case of clustering is discussed and a general method is proposed and its convergence is proved. Also, it is proved that the FCM and the FCV methods are special cases of the proposed method. Based on the general method, a special case for color image processing is proposed. The clustering method is based on a likelihood measure, and is proved to outperform the Euclidean and the Mahalanobis distances, in color fields. Based on the proposed color clustering method, a new fast fuzzy segmentation method is proposed and is proved to be highly efficient Comparison of the results with the FCM, proves the superiority of the proposed segmentation method.
Starting Page	72
Ending Page	75
File Size	953760
Page Count	4
File Format	PDF
ISBN	0780386892
DOI	10.1109/ISSPIT.2004.1433691
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-18
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Color Clustering methods Euclidean distance Clustering algorithms Covariance matrix Phase change materials Principal component analysis Mathematics Convergence
Content Type	Text
Resource Type	Article

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