基于轮廓形状和球面距离的复杂网络模型拓扑性质研究

Topological Properties of Complex Network Models Based on Contour Shape and Sphere Distance

复杂网络是近几年来受到国内外广泛关注的高新技术,在图像识别领域也取得了广泛应用。复杂网络主要根据图像轮廓形状特征建立网络模型,并提取拓扑特征参数进行图像识别。本文针对形状轮廓建立的复杂网络模型特性来进行测试,验证了基于球面距离建立的复杂网络模型非常符合复杂网络度分布的拓扑特征,并且具有旋转、缩放不变性等特征,而且图像识别准确率较高。

Complex network is a new technology which has been widely concerned at home and abroad in recent years. It has also been widely applied in the field of image recognition. Complex network is mainly based on image contour and shape feature to establish network model,and extract topological feature parameters for image recognition. In this paper,the complex network model characteristics of the shape contour are tested,and the complex network model based on the spherical distance is proved to be very consistent with the topological features of the complex network degree distribution,and has the characteristics of rotation,scaling invariance and so on,and the accuracy of image recognition is high.