Utilization of grey level co-occurrence matrix and Markov random field model for segmentation of colour textured images

Authors:
J. Mridula;Dipti Patra
Affiliations:
National Institute of Technology Rourkela, Rourkela, Odisha, India;National Institute of Technology Rourkela, Rourkela, Odisha, India
Venue:
Proceedings of the 2011 International Conference on Communication, Computing & Security
Year:
2011

Citing 3
Cited 0

Multicue MRF Image Segmentation: Combining Texture and Color Features

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)

The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
A stochastic method for Bayesian estimation of hidden Markov random field models with application to a color model

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a new approach for color textured image segmentation. It is a two stage technique, where in the first stage, textural features using gray level co-occurrence matrix are computed for regions of interest (ROI) considered for each class. ROI act as ground truths for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Mean at inter pixel distance (IPD) 1 of I2 component was found to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and MRF model is employed to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of using GLCM and Maximum Likelihood classifier as proposed by P. V. Narasimha Rao et. al. and with that which uses GLCM and MRF in RGB colour space. The proposed method is found to be better in terms of accuracy than the other two methods.