融合边缘特征的马尔可夫随机场模型及分割算法

徐胜军 1; 2; 韩九强 1; 何波 2; 赵亮 2; 肖海燕 2

doi:10.7652/xjtuxb201402003

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融合边缘特征的马尔可夫随机场模型及分割算法

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- 融合边缘特征的马尔可夫随机场模型及分割算法
- A Region Markov Random Field Model with Integrated Edge Feature and Image Segmentation Algorithm
- 西安交通大学学报 2014年48卷第2期页码：14-19
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室,西安,710049
  2. 西安建筑科技大学信息与控制工程学院,西安,710055
- 作者简介：
- 基金信息：
  
  : 2013-10-28。作者简介: 徐胜军(1976—),男,博士生;韩九强(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金青年基金资助项目(51209167);陕西省自然科学基金资助项目(2012JM8026,2013JM8030);陕西省教育厅专项基金资助项目(2013JK1091)
- DOI：10.7652/xjtuxb201402003
  中图分类号： TP393
- 网络首发：2014-02-10，
  
  纸质出版：2014
- 稿件说明：
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徐胜军 1, 2, 韩九强 1, 等. 融合边缘特征的马尔可夫随机场模型及分割算法[J]. 西安交通大学学报, 2014,48(2):14-19.

A Region Markov Random Field Model with Integrated Edge Feature and Image Segmentation Algorithm[J]. 2014, 48(2): 14-19.
徐胜军 1, 2, 韩九强 1, 等. 融合边缘特征的马尔可夫随机场模型及分割算法[J]. 西安交通大学学报, 2014,48(2):14-19. DOI： 10.7652/xjtuxb201402003.

A Region Markov Random Field Model with Integrated Edge Feature and Image Segmentation Algorithm[J]. 2014, 48(2): 14-19. DOI： 10.7652/xjtuxb201402003.

摘要

针对区域马尔可夫随机场(MRF)模型的图像分割中常产生边缘模糊的问题

提出了一种融合边缘特征的区域MRF模型(IEFRMRF)及其分割算法。IEFRMRF模型基于MRF理论

首先通过边缘模板提取图像的边缘特征

建立局部区域的边缘先验约束; 其次利用图像局部区域像素的空间约束关系描述图像的局部高斯统计特征

并通过期望最大化算法估计高斯特征参数; 然后根据贝叶斯原理建立了具有边缘保持作用的区域MRF模型; 最终采用区域置信度传播(BP)算法对IEFRMRF模型进行全局优化

把局部统计特征传递到图像的全局

并按照MAP准则估计图像分割标号。人工加噪声图像分割的实验结果表明

IEFRMRF模型的分割结果和传统高斯MRF模型、局部区域高斯MRF模型的分割结果相比

分割准确率分别提高了47.9%和21.4%

并且分割结果的边缘更清晰

自然图像的分割实验也验证了提出模型的有效性。

Abstract

A region Markov random field model with integrated edge feature(IEFRMRF)is proposed to solve the problem that existing region Markov random fields(MRF)model often leads to produce blur edge in image segmentation. The proposed model utilizes edge templates to extract edge features of an image

and builds edge prior constraints on local regions. Space constraints among local regions of the image are used to express local Gaussian statistical features of the image

and the Gaussian parameters are estimated by maximizing expectations. Then a new local adaptive neighborhood information Gaussian mixture model(GMM)is constructed and an algorithm is proposed to estimate its parameters. The region MRF model that preserves image edge is then built based on the Bayesian theory. The region belief propagation algorithm is applied to globally optimize the IEFRMRF model. Local statistical features are transferred to the image of the global

and image segmentation labels are estimated by MAP criterion during the optimization. Experiments on an artificial noise image and comparisons with the classical Gaussian MRF model and the local region Gaussian MRF model show that the IEFRMRF model not only increases segmentation accuracy rate by 47.9% and 21.4%

respectively

but also acquires sharper edge of segmentation result. The validity of the proposed model is also verified by natural image segmentation experiments.

关键词

Keywords

references

姚婷婷, 谢昭. 多层次MRF重标记及映射法则下的图像分割[J]. 自动化学报, 2012, 38(9): 1-13.

YAO Tingting, XIE Zhao. Top-down inference with relabeling and mapping rules in hierarchical MRF for image segmentation[J]. Acta Automatica Sinica, 2012, 38(9): 1-13.

CHEN A Y C, CORSO J J, WANG L. HOPS: efficient region labeling using higher order proxy neighborhoods[C]∥Proceedings of International Conference on Pattern Recognition. Los Alamitos, CA, USA: IEEE Computer Society, 2008: 1-4.

徐胜军, 韩九强, 赵亮, 等. 用于图像分割的局部区域能量最小化算法[J]. 西安交通大学学报, 2011, 45(8): 7-12.

XU Shengjun, HAN Jiuqiaug, ZHAO Liaug, et al. Algorithm of minimizing local region energy for image segmentation[J]. Journal of Xi'an Jiaotong University, 2011, 45(8): 7-12.

GAO Qi, ROTH S. How well do filter-based MRFs model natural images?[C]∥ Proceedings of DAGM/OAGM Symposium. Berlin Germany: Springer, 2012: 62-72.

张志, 王润生. 边缘保持专家场模型的自然图像复原[J]. 自然科学进展, 2009, 19(9): 1004-1013.

ZHANG Zhi, WANG Runsheng. Nature image restoration based on edge preserving FoE model[J]. Progress in Natural Science, 2009, 19(9): 1004-1013.[6] KORAY K, ERCAN E K, BÜLENT S. Bayesian separation of images modeled with MRF using MCMC[J]. IEEE Transactions Image Process, 2009, 18(5): 982-994.

ZHANG Haichao, ZHANG Yanning, LI Haisen, et al. Generative Bayesian image super resolution with natural image prior[J]. IEEE Transactions on Image Processing, 2012, 21(9): 4054-4066.

KATSUKI T, TORII A, INOUE M. Posterior mean super-resolution with a causal Gaussian Markov random field prior[J]. IEEE Transactions on Image Processing, 2012, 21(7): 3182-3193.

LI S Z. Markov random field modeling in computer vision[M]. Berlin, Germany: Springer-Verlag, 2001.

LEUNG Shingyu, LIANG Gang, SOLNA K, et al. Expectation-maximization algorithm with local adaptivity[J]. SIAM Journal on Imaging Sciences, 2009, 2(3): 834-857.

LIN Jun, ZHANG Haili. Image segmentation using a local GMM in a variational framework[J]. Journal of Mathematical Imaging and Vision, 2013, 46(2): 161-176.

GREENSPAN H, RUF A, GOLDBERGER J. Constrained Gaussian mixture model framework for automatic segmentation of MR-brain image[J]. IEEE Transactions on Medical Imaging, 2006, 25(9): 1233-1245.

TANG Hui, DILLENSEGER J L, LUO Limin. A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model[J]. Computerized Medical Imaging and Graphics, 2009, 33(8): 644-650.

朱峰, 罗立民, 宋余庆, 等. 基于自适应空间邻域信息高斯混合模型的图像分割[J]. 计算机研究与发展, 2011, 48(11): 2000-2007.

ZHU Feng, LUO Limin, SONG Yuqing, et al. Adaptive spatially neighborhood information Gaussian mixture model for image segmentation[J]. Journal of Computer Research and Development, 2011, 48(11): 2000-2007.

YEDIDIA J, FREEMAN W, WEISS Y. Generalized belief propagation[C]∥Proceedings of Conference on Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2000: 689-695.

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