Improved Image Segmentation With A Modified Bayesian Classifier

• Published in: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings Vol. 2; p. II
• Main Author: Weldon, T.P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Bandwidth; Bayesian methods; Degradation; Filter bank; Image edge detection; Image segmentation; Multidimensional systems; Statistics; Surface texture; Welding
• ISBN: 9781424404698; 142440469X
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP.2006.1660438

A method for improving texture segmentation results by slightly modifying the decision surfaces of a Bayesian classifier is presented. Although a Bayesian classifier provides optimum classification within homogeneous regions, it does not necessarily provide accurate localization of region boundaries. In the proposed method, a modified classifier is formed by using a mixture probability density. This approach has the advantage that it is easily implemented in multidimensional classifiers such as those used in classifying the vector output of a filter bank. Experimental results demonstrate improved texture segmentation using the proposed classifier