Volume 2 Number 10 (Dec. 2007)
Home > Archive > 2007 > Volume 2 Number 10 (Dec. 2007) >
JCP 2007 Vol.2(10): 1-8 ISSN: 1796-203X
doi: 10.4304/jcp.2.10.1-8

Partitional Clustering Techniques for Multi-Spectral Image Segmentation

Danielle Nuzillard1, Cosmin Lazar2
1CReSTIC, UFR Sciences, Moulin de la Housse, University of Reims 51687 Reims cedex 2, France
2IFTS, University of Reims, 07 Bd. Jean Delautre, 08000 Charleville-M´ezi`eres, France


Abstract—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

[PDF]

Cite: Danielle Nuzillard, Cosmin Lazar, "Partitional Clustering Techniques for Multi-Spectral Image Segmentation," Journal of Computers vol. 2, no.10, pp. 1-8, 2007.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
  • Nov 14, 2019 News!

    Vol 14, No 11 has been published with online version   [Click]

  • Mar 20, 2020 News!

    Vol 15, No 2 has been published with online version   [Click]

  • Dec 16, 2019 News!

    Vol 14, No 12 has been published with online version   [Click]

  • Sep 16, 2019 News!

    Vol 14, No 9 has been published with online version   [Click]

  • Aug 16, 2019 News!

    Vol 14, No 8 has been published with online version   [Click]

  • Read more>>