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SMIXTURE: strategy for mixture model clustering of multivariate images
Author(s) -
Tran Thanh N.,
Wehrens Ron,
Buydens Lutgarde M. C.
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.966
Subject(s) - cluster analysis , outlier , mixture model , multivariate statistics , hierarchical clustering , pattern recognition (psychology) , cluster (spacecraft) , mixture distribution , artificial intelligence , computer science , single linkage clustering , multivariate normal distribution , noise (video) , segmentation , determining the number of clusters in a data set , data mining , mathematics , fuzzy clustering , cure data clustering algorithm , image (mathematics) , statistics , probability density function , machine learning , programming language
SMIXTURE, a novel strategy for mixture model clustering of multivariate images, has been developed. Most other clustering approaches require good guesses of the number of components (clusters) and the initial statistical parameters. In our approach, the initial parameters are determined by agglomerative clustering on homogenous regions, identified by region growing segmentation. SMIXTURE can be used in both a normal situation of mixture modeling, where the density of a cluster is modeled by a single normal distribution; and in a more complex situation, where the density of a single cluster is a mixture of several normal sub‐clusters. The method has proven to be very robust to noise/outliers, overlapping clusters, is reasonably fast and is suitable for moderate to large images. Copyright © 2006 John Wiley & Sons, Ltd.