Texture segmentation using SOM and multi-scale bayesian estimation

Authors:
Tae Hyung Kim;Il Kyu Eom;Yoo Shin Kim
Affiliations:
Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Dept. Electronics Engineering, Pusan National University, Busan, Republic of Korea;Research Institute of Computer, Information and Communication, Pusan National Univesity, Busan, Republic of Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 6
Cited 0

Unsupervised texture segmentation using Gabor filters

Pattern Recognition
Handbook of Pattern Recognition and Computer Vision

Handbook of Pattern Recognition and Computer Vision
Support Vector Machines for Texture Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Improved hidden Markov models in the wavelet-domain

IEEE Transactions on Signal Processing
Multiscale image segmentation using wavelet-domain hidden Markov models

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a likelihood estimation method from SOM (self organizing feature map), and texture segmentation is performed by using Bayesian estimation and SOM. Multi-scale wavelet coefficients are used as input for SOM, and likelihood probabilities for observations are obtained from trained SOMs. Texture segmentation is performed by the likelihood probability from trained SOMs and ML (maximum likelihood) classification. The result of texture segmentation is improved using contextual information. The proposed segmentation method performed better than segmentation method using HMT (hidden Markov trees) model. In addition, texture segmentation results by SOM and multi-scale Bayesian image segmentation technique called HMTseg also performed better than those by HMT and HMTseg.