A genetic algorithm for MRF-based segmentation of multi-spectral textured images

被引：32

作者：

Tseng, DC ^{[1
]}

Lai, CC ^{[1
]}

机构：

[1] Natl Cent Univ, Inst Comp Sci & Informat Engn, Chungli 320, Taiwan

来源：

PATTERN RECOGNITION LETTERS | 1999年 / 20卷 / 14期

关键词：

unsupervised texture segmentation; Markov random field; genetic algorithm; multi-spectral remote-sensing images;

D O I：

10.1016/S0167-8655(99)00117-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. Tn this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multispectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：1499 / 1510

页数：12

共 50 条

[21] Precise acquisition and unsupervised segmentation of multi-spectral images
Delgado Gomez, David
Clemmensen, Line Harder
Ersboll, Bjarne K.
Carstensen, Jens Michael
COMPUTER VISION AND IMAGE UNDERSTANDING, 2007, 106 (2-3) : 183 - 193
[22] Multiscale MRF-based texture segmentation of SAR image
Xu, X
Li, DR
Sun, H
CHINESE JOURNAL OF ELECTRONICS, 2004, 13 (04): : 671 - 675
[23] Method for multi-spectral images segmentation based on the shape of the color clusters.
Kroupnova, NH
IMAGING SCIENCES AND DISPLAY TECHNOLOGIES, 1997, 2949 : 444 - 453
[24] Minimum entropy segmentation applied to multi-spectral chromosome images
Schwartzkopf, W
Evans, BL
Bovik, AC
2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, 2001, : 865 - 868
[25] Unsupervised evaluation of image segmentation application to multi-spectral images
Chabrier, S
Emile, B
Laurent, H
Rosenberger, C
Marché, P
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1, 2004, : 576 - 579
[26] An MRF-Based Multigranularity Edge-Preservation Optimization for Semantic Segmentation of Remote Sensing Images
Zheng, Chen
Chen, Yuncheng
Shao, Jie
Wang, Leiguang
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
[27] Designated Target Enhancement and Segmentation in Multi-spectral MR Images
Yang, Sheng-Chih
Wun, Yi-Jyun
He, Yue-Jing
2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C), 2016, : 1059 - 1062
[28] Segmentation of multi-spectral images using the combined classifier approach
Paclík, P
Duin, RPW
van Kempen, GMP
Kohlus, R
IMAGE AND VISION COMPUTING, 2003, 21 (06) : 473 - 482
[29] Automatic segmentation of multi-spectral MR brain images using a neuro-fuzzy algorithm
Byun, HS
Ham, YK
Kim, YH
CARS 2000: COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2000, 1214 : 1015 - 1015
[30] Unsupervised segmentation of color textured images using a multi-layer MRF model
Kato, Z
Pong, TC
Qiang, SG
2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 1, PROCEEDINGS, 2003, : 961 - 964

← 1 2 3 4 5 →