一种基于复杂网络的图像形状及纹理描述方法

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 244-246.

一种基于复杂网络的图像形状及纹理描述方法

洪睿, 康晓东, 李博, 王亚鸽

天津医科大学医学影像学院天津300203

出版日期:2019-02-26 发布日期:2019-02-26
作者简介:洪睿(1993-),男,硕士生,主要研究方向为图像处理;康晓东(1964－),博士,教授,CCF高级会员,主要研究方向为医学图像处理、医疗信息系统集成;李博(1987-),硕士生,主要研究方向为医疗信息系统集成;王亚鸽(1992-),硕士生,主要研究方向为图像处理。
基金资助:
本文受天津市重点基金(17JC20J32500)资助。

Image Shape and Texture Description Method Based on Complex Network

HONG Rui, KANG Xiao-dong, LI Bo, WANG Ya-ge

School of Medical Image,Tianjin Medical University,Tianjin 300203,China

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

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。

关键词: 动态演化, 度矩阵, 复杂网络, 图像纹理, 最小生成树

Abstract: 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,anduse 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.

Key words: Complex network, Degree matrix, Dynamic evolution, Image texture, Minimum spanning tree

中图分类号:

TN911.73

洪睿, 康晓东, 李博, 王亚鸽. 一种基于复杂网络的图像形状及纹理描述方法[J]. 计算机科学, 2018, 45(11A): 244-246. https://doi.org/

HONG Rui, KANG Xiao-dong, LI Bo, WANG Ya-ge. Image Shape and Texture Description Method Based on Complex Network[J]. Computer Science, 2018, 45(11A): 244-246. https://doi.org/

参考文献

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