计算机科学 ›› 2018, Vol. 45 ›› Issue (11): 272-277.doi: 10.11896/j.issn.1002-137X.2018.11.043
柴五一, 杨丰, 袁绍锋, 黄靖
CHAI Wu-yi, YANG Feng, YUAN Shao-feng, HUANG Jing
摘要: 高斯混合模型是一种简单有效且被广泛使用的图像分割工具。然而,传统的高斯混合模型在混合成分个数确定时的拟合结果不够精确;此外,由于没有考虑像素间的空间关系,导致分割结果易受噪声干扰,且分割精度不高。为弥补传统高斯混合模型的缺陷,文中提出多分类高斯混合模型和基于邻域信息的高斯混合模型用于图像分割。多分类高斯混合模型对传统混合模型进行二重分解:传统混合模型由M个分布加权混合得到,多分类混合模型进一步将M个分布中的每一个分布分解成R个分布。即多分类高斯混合模型由M个高斯分布混合组成,而这M个分布分别由R个不同的分布混合得到,提高了模型的拟合精度。基于邻域信息的高斯混合模型通过对模型中的先验概率和后验概率添加空间信息约束,增强了像素间的信息关联和抗噪性。采用结构相似性、误分率和峰值信噪比等指标来评价分割结果。通过实验发现:与现有的混合模型分割方法相比,文中方法大幅提高了分割精度,且有效地抑制了噪声干扰。
中图分类号:
[1]WANG L X,XIE W X,PEI J H.Ocean borne image segmentation using muti Gaussian mixture model covering feature space learning[J].Transactions of Electronics,2014,42(10):2117-2122.(in Chinese) 王荔霞,谢维信,裴继红.多高斯模型特征空间覆盖学习的海洋航摄图像分割[J].电子学报,2014,42(10):2117-2122. [2]SUN Q S,JI Z X.Overview of brain magnetic resonance image segmentation algorithms based on fuzzy clustering [J].Data Acquisition and Processing,2016,31(1):28-42.(in Chinese) 孙权森,纪则轩.基于模糊聚类的脑磁共振图像分割算法综述[J].数据采集与处理,2016,31(1):28-42. [3]ZHANG M H,GUO Z W,LIU Y.Segmentation algorithm based on mixture model for SAR images of land and sea [J].Photoelectron.Laser,2017,28(3):326-333.(in Chinese) 张苗辉,郭拯危,刘扬.基于混合模型的 SAR 影像海陆分割算法[J].光电子.激光,2017,28(3):326-333. [4]ZHANG L,GAN C S,HU Y.Research on ship detection algorithms for high resolution optical remote sensing images [J].Computer Engineering and Applications,2017,53(9):184-189.(in Chinese) 张雷,甘春生,胡宇.高分辨率光学遥感影像舰船检测算法研究[J].计算机工程与应用,2017,53(9):184-189. [5]章毓晋.图像分割[M].北京:科学出版社,2001. [6]CAI W L.Research on image segmentation and classifier design based on clustering [D].Nanjing:Nanjing University of Aeronautics & Astronautics,2008.(in Chinese) 蔡维玲.基于聚类的图像分割和分类器设计的研究[D].南京:南京航空航天大学,2008. [7]ZHAO Q H,SHI X,WANG Y,et al.Remote sensing image segmentation by Gaussian mixture model with variable spatial constraint [J].Journal of Communications,2017,38(2):34-43.(in Chinese) 赵泉华,石雪,王玉,等.可变类空间约束高斯混合模型遥感图像分割[J].通信学报,2017,38(2):34-43. [8]SANJAY-GOPAL S,HEBERT T J.Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm [J].IEEE Transactions on Image Processing,1998,7(7):1014-1028. [9]NIKOU C,GALATSANOS N P,LIKAS A C.A class-adaptive spatially variant mixture model for image segmentation [J].IEEE Transactions on Image Processing,2007,16(4):1121-1130. [10]YANG Y F.Research on application of medical image segmentation algorithms based on finite mixture model[D].Nanjing:Southeast University,2015.(in Chinese) 杨云飞.基于有限混合模型的医学图像分割算法应用研究[D].南京:东南大学,2015. [11]ZHANG H,WU Q M J,NGUYEN T M.Incorporating mean template into finite mixture model for image segmentation [J].IEEE Transactions on Neural Networks and Learning Systems,2013,24(2):328-335. [12]PRAKASH R M,KUMARI R S S.Gaussian Mixture Model with the Inclusion of Spatial Factor and Pixel Re-labelling:Application to MR Brain Image Segmentation [J].Arabian Journal for Science & Engineering,2017,42(2):595-605. [13]VEGAS-SANCHEZ-FERRERO G,SEABRA J,RODRIGUEZ-LEOR O,et al.Gamma mixture classifier for plaque detection in intravascular ultrasonic images[J].IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control,2014,61(1):44-61. [14]COPSEY K,WEBB A.Bayesian gamma mixture model approach to radar target recognition [J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1201-1217. [15]QIN Y,PRIEBE C E.Maximum L q-Likelihood Estimation via the Expectation-Maximization Algorithm:A Robust Estimation of Mixture Models [J].Journal of the American Statistical Association,2013,108(503):914-928. [16]COMER M,BOUMAN C A,DE GRAEF M,et al.Bayesian methods for image segmentation [J].JOM Journal of the Mi-nerals,Metals and Materials Society,2011,63(7):55-57. [17]ZHANG M.Bilateral filter in image processing [J].Applied Surface Science,2006,253(2):468-475. [18]MARTIN D,FOWLKES C,TAL D,et al.A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]∥Proceeding of International Conference on Computer Vision.2001:416-423. |
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