模糊相关图割的非监督层次化彩色图像分割  

Unsupervised hierarchical color image segmentation through fuzzy correlation and graph cut

作　　者：尹诗白[1] 孔垂涵 王一斌[2] Yin Shibai;Kong Chuihan;Wang Yibin(Department of Economic Information Engineering,Southwestern University of Finance and Economics,Chengdu 611130,China;Department of Engineering,Sichuan Normal University,Chengdu 610101,China)

机构地区：[1]西南财经大学经济信息工程学院,成都611130 [2]四川师范大学工学院,成都610101

出　　处：《中国图象图形学报》2018年第9期1326-1334,共9页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61502396);西南财经大学中央高校基本科研业务费专项基金项目(JBK150503);西南财经大学中央高校基本科研基金项目(JBK1801076);四川省教育厅一般基金项目(18ZB0484);四川师范大学自制仪器设备基金项目(ZZYQ2017001);陕西省科技厅工业公关项目(2016GY-088);互联网金融创新及监管四川省协同创新中心;金融智能与金融工程四川省重点实验室资助项目~~

摘　　要：目的基于阈值的分割方法能根据像素的信息将图像划分为同类的区域,其中常用的最大模糊相关分割方法,因能利用模糊相关度量划分的适当性,得到较好的分割结果,而广受关注。然而该算法存在划分数需预先确定,阈值的分割结果存在孤立噪声,无法对彩色图像实施分割的问题。为此,提出基于模糊相关图割的非监督层次化分割策略来解决该问题。方法算法首先将图像划分为若干超像素,以提高层次化图像分割的效率;随后将快速模糊相关算法与图割结合,构成模糊相关图割2-划分算子,在确保分割效率的基础上,解决单一阈值分割存在孤立噪声的问题;最后设计了自顶向下层次化分割策略,利用构建的2-划分算子选择合适的区域及通道,迭代地对超像素实施层次化分割,直到算法收敛,划分数自动确定。结果对Berkeley分割数据库上300幅图像进行了测试,结果表明算法能有效分割彩色图像,分割精度优于Ncut、JSEG方法,运行时间较这两种方法也提高了近20%。结论本文算法为最大模糊相关算法在非监督彩色图像分割领域的应用提供指导依据,能用于目标检测和识别领域。Objective Image segmentation is a process of dividing an image into different regions such that each region is homogeneous but the union of any two adjacent regions is not. As the first step in image analysis and pattern recognition,image segmentation serves as a fundamental step in numerous computer vision applications,such as object detection,content-based image retrieval,and medical image analysis. Threshold-based methods,which subdivide the image into several homogenous regions on the basis of pixel intensities,are popular segmentation techniques. Numerous algorithms have been proposed in this direction,which include gray-level thresholding and interactive pixel classification. Among these algorithms,the frequently used maximum fuzzy correlations are widely adopted to measure the appropriateness of fuzzy two partitions for the image segmentation purpose due to the unavoidable ambiguities,fuzziness,and uncertainty of the image information. However,this method has some limitations,i. e.,the partition number needs to be preset,the results have isolated noise,and maximum fuzzy correlation approach cannot be extended to color image segmentation. Method Most existing gray-level image segmentation techniques could be extended to color image. They can be directly applied to each component of a color space,and then,the result can be combined in a certain way to obtain the final segmentation result. However,one of the problems is how to use the color information as a whole for each pixel and how to select the color representation for segmentation because each color representation has advantages and disadvantages. To address these problems,an unsupervised hierarchical color image segmentation through maximum fuzzy correlation and graph cut is proposed. First,we oversegment the color image into superpixels to improve the efficiency of hierarchical image segmentation. Then,we combine the fast fuzzy correlation with graph cut to form a bi-level segmentation operator,which can suppress the isolated noise caused by the singl

关 键 词：彩色图像分割 非监督分割 超像素 模糊相关 图割 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]