一种基于复杂网络的图像形状及纹理描述方法  

Image Shape and Texture Description Method Based on Complex Network

作　　者：洪睿[1] 康晓东[1] 李博 王亚鸽 HONG Rui;KANG Xiao-dong;LI Bo;WANG Ya-ge(School of Medical Image,Tianjin Medical University,Tianjin 300203,China)

机构地区：[1]天津医科大学医学影像学院,天津300203

出　　处：《计算机科学》2018年第B11期244-246,共3页Computer Science

基　　金：天津市重点基金(17JC20J32500)资助

摘　　要：文中提出了一种基于复杂网络的图像特征描述方法。将图像的关键点作为复杂网络节点,利用最小生成树分解法完成初始网络的动态演化过程,由不同演化阶段下的复杂网络特征实现对图像的形状描述;根据图像像素和周围邻域的距离与灰度的相似度,由不同的阈值生成度矩阵,统计不同阈值下网络节点的度分布,完成图像的纹理描述。实验证明,该算法具有较强的鲁棒性和旋转不变性,并且在分类实验中也有较好的表现。This paper proposed an image feature description method based on complex network.By using the key points of the image as the node of complex network,this method uses MST measure to achieve dynamic evolution process,and use complex network characters in different phase to achieve the description of the shape of the image.With the distance and the difference of gray level between a pixel and its neighborhood,a series of degree matrices can be represented by using a series of thresholds,and the texture feature can be represented by calculating the degree distribution of network nodes under different thresholds.This method is based on statistical image description method.It has stronger robustness and rotation invariance,and has a great performance in classification experiments.

关 键 词：图像纹理 复杂网络 最小生成树 度矩阵 动态演化 

分 类 号：TN911.73[电子电信—通信与信息系统]