摘要
本文结合复杂网络理论分析形状特性,对形状建模成基于形状内部距离的小世界复杂网络,分析复杂网络节点的度特征和聚类系数特征,通过复杂网络的动态演化,构造多尺度的关于节点分布的直方图来描述形状;用轮廓点多尺度的度特征、聚类系数特征和内部距离形状上下文特征来描述轮廓点,结合改进的最优子序列双射算法实现形状匹配.实验中分析了复杂网络理论中各特征在形状分析中的描述能力,实验结果表明提出的形状匹配算法能实现一些非刚性变换下的物体形状匹配和较高精度的形状检索.
This paper uses complex network theory to analyze the shape characteristics.Based on inner distance of shape,a shape is modeled into a small-world complex network,through analyze degree and cluster coefficient characteristics of nodes in the dynamic evolution of complex network,multi-scale histograms on nodes distribution is proposed for shape descriptor;describe contour points of shape with multi-scale degree、cluster coefficient characteristics and shape context features based on inner distance,this paper combines the improved optimal subsequence bijection to achieve shape matching.Experiment analyzes the capacity of complex network characteristics for shape descriptor,experimental results show that the proposed algorithm can achieve shape matching with non-rigid transformations and the high precision in shape retrieval.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2011年第8期1757-1765,共9页
Acta Electronica Sinica
基金
国家自然科学基金(No.60772122)
安徽省教育厅自然科学重点基金(No.KJ2008A033&&KJ2010A006)
关键词
形状描述
复杂网络
最优子序列双射
形状匹配
shape descriptor
complex network
optimal subsequence bijection
shape retrieval
