Comparison of Two Unsupervised Methods of Classification for Segmenting Multi-Spectral Images

Author

Nuzillard, Danielle ; Lazar, Cosmin

Author_Institution

CReSTIC, Champagne-Ardenne Univ., Reims

Volume

3

fYear

2006

fDate

14-19 May 2006

Abstract

Some clustering algorithms require assumptions (such as number and shape of classes), which limit their performances or provide wrong results. On the contrary, methods based on the estimation of the probability density function (pdf) do not make any assumption neither on the classes shape nor on their number. Two methods based on the pdf, are explored and applied to the segmentation of a multi-spectral image of a cereal grain. The first one is inspired from the estimation of the pdf Parzen-Rosenblatt and the second one estimates the support of the pdf through the support vector theory

Keywords

image classification; image segmentation; probability; clustering algorithms; image classification; image segmentation; multispectral images; probability density function; support vector theory; unsupervised methods; Chemical lasers; Clustering algorithms; Image segmentation; Independent component analysis; Kernel; Laser beam cutting; Multispectral imaging; Probability density function; Shape; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660773

Filename

1660773