Improved hierarchical Markov random field algorithm color image segmentation algorithm

doi:10.11772/j.issn.1001-9081.2016.09.2576

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2576-2579.DOI: 10.11772/j.issn.1001-9081.2016.09.2576

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Improved hierarchical Markov random field algorithm color image segmentation algorithm

WANG Lei, HUANG Chenxue

Hubei Agricultural Machinery Engineering Research and Design Institute, Hubei University of Technology, Wuhan Hubei 430068, China

Received:2016-02-26 Revised:2016-04-12 Online:2016-09-10 Published:2016-09-08
Supported by:
This work was partially supported by Natural Science Foundation of Hubei Province of China (Grant No. 2014CFB583). the National Natural Science Foundation of China (51174084).

改进的分层马尔可夫随机场彩色图像分割算法

王雷, 黄晨雪

湖北工业大学湖北省农业机械工程研究设计院, 武汉 430068

通讯作者: 王雷
作者简介:王雷(1986-),男,湖北宜昌人,讲师,博士,主要研究方向:图像分割、图像识别、模糊控制、机器学习、数据挖掘;黄晨雪(1993-),女,湖北荆州人,硕士研究生,主要研究方向:彩色图像分割、机器学习、数据挖掘。
基金资助:
国家自然科学基金资助项目（51174084）；湖北省自然科学基金资助项目（2014CFB583）。

Abstract

Abstract: The distribution of color image pixel value is difficult to describe in hierarchical Markov Random Field (MRF) segmentation algorithm, therefore, a hierarchical MRF segmentation algorithm based on RGB color statistic distribution was proposed to solve this problem. The key parameters of the MRF model were set up, and the related formulas were deduced. With the RGB color statistic distribution model, the hierarchical MRF energy function was rewritten, and the k-means algorithm was used as presegmentation method to realize unsupervised segmentation. The proposed algorithm has fewer color distribution parameters and lower computational cost in comparison with traditional MRF segmentation model, which describes color distribution more accurately; and it can describe different targets and background very well without being restricted by target and background color distribution and target spatial distribution. Experimental results prove the effectiveness of the proposed algorithm, which is superior to the MRF algorithm and Fuzzy C-Means (FCM) algorithm in computing speed and segmentation accuracy.

Key words: color image, hierarchical Markov Random Field (MRF), Red, Green and Blue color space, image segmentation, energy function

摘要： 针对传统的分层马尔可夫随机场（MRF）算法难以描述彩色图像像素值分布等问题，提出一种基于RGB色彩统计分布的分层MRF分割算法。在分层MRF模型的基础上，设定了相关参数并对分割过程进行了公式推导；结合RGB色彩统计分布模型，重写了分层MRF能量函数，利用k-means算法作为预分割算法，实现了算法的无监督分割。相比传统的分层MRF分割模型，该算法充分利用了彩色图像的像素值的信息，可有效地减少颜色分布参数和计算成本，能更准确地描述各分割对象的颜色分布；且该算法不受目标和背景颜色区间分布、目标空间分布的限制，能够很好地描述不同目标和背景。通过大量实验验证了算法的有效性，其在运算速度、分割精度等方面均优于传统MRF算法和模糊C均值（FCM）算法。

关键词: 彩色图像, 分层MRF, RGB色彩空间, 图像分割, 能量函数

CLC Number:

TN919.8

WANG Lei, HUANG Chenxue. Improved hierarchical Markov random field algorithm color image segmentation algorithm[J]. Journal of Computer Applications, 2016, 36(9): 2576-2579.

王雷, 黄晨雪. 改进的分层马尔可夫随机场彩色图像分割算法[J]. 计算机应用, 2016, 36(9): 2576-2579.

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Improved hierarchical Markov random field algorithm color image segmentation algorithm

改进的分层马尔可夫随机场彩色图像分割算法

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