ISSN : 1796-203X
Volume : 2    Issue : 10    Date : December 2007

Partitional Clustering Techniques for Multi-Spectral Image Segmentation
Danielle Nuzillard and Cosmin Lazar
Page(s): 1-8
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Analyzing unknown data sets such as multispectral images often requires unsupervised
techniques. Data clustering is a well known and widely used approach in such cases. Multi-spectral
image segmentation requires pixel classification according to a similarity criterion. For this
particular data, partitional clustering seems to be more appropriate. Classical K-means algorithm  
has important drawbacks with regard to the number and the shape of clusters. Probability density
function based methods overcome these drawbacks and are investigated in this paper. Two main
steps in data clustering are dimension reduction and data representation. Methods like PCA and
ICA often perform dimension reduction step. To achieve a complete and more reliable
representation of the data, a magnitude-shape representation is described, it takes into account
both the magnitude and shape similarities between pixels vectors. The bases of PCA and
magnitude-shape representation are explored to highlight the main differences and the advantages
of our method over PCA. Experimental results confirm that this method is a reliable alternative to
classical linear projection methods for dimension reduction.

Index Terms
multicomponent data, probability density function, dimension reduction, partitional clustering,
similarity measures, magnitude-shape representation.