Supervised fuzzy and Bayesian classification of high dimensional data: a comparative study

Author

Mostafa, Mostafa G H ; Perkins, Timothy C. ; Farag, Aly A.

Author_Institution

Comput. Vision & Image Process. Lab., Louisville Univ., KY, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

772

Abstract

This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce the computational time. Comparison with the conventional FCM clustering technique and the Bayesian classification technique is also presented. Performance results of the three algorithms are presented on simulated and real remote sensing multispectral data, which show improvement in the classification accuracy using the SFCM technique

Keywords

Bayes methods; computational complexity; fuzzy systems; image classification; learning (artificial intelligence); remote sensing; spectral analysis; Bayesian classification; FCM clustering; SFCM technique; classification accuracy; computational time reduction; high dimensional data; image classification; iterative classifier; noniterative classifier; performance results; real remote sensing multispectral data; simulated remote sensing multispectral data; supervised fuzzy c-mean classifier; supervised fuzzy classification; Bayesian methods; Classification algorithms; Clustering algorithms; Computer vision; Fuzzy logic; Image processing; Iterative algorithms; Pixel; Remote sensing; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.901073

Filename

901073