摘要
本文提出了一种基于典型时间规整(canonical time warping,CTW)算法的形状相似性度量模型,该模型运用动态时间规整(dynamic time warping,DTW)算法和典型相关分析(canonical correlation analysis,CCA),对齐具有不同节点个数的建筑物坐标序列,并综合评价不同形状轮廓之间的相似性。该方法直接以矢量坐标作为模型输入而无须复杂的形状编码,顾及了建筑物图形原始的轮廓特征,可高效地应用于形状检索等场景。试验表明,CTW算法在度量形状相似性时具有平移、旋转、缩放和镜像不变性,能有效度量建筑物形状之间的形状相似性,其结果符合人类的空间视觉认知。
This paper proposes a shape similarity measurement model based on canonical time warping(CTW)algorithm.The model combines canonical correlation analysis(CCA)and dynamic time warping(DTW)to align building coordinate sequences with different number of vertices,which can comprehensively evaluate the shape similarity between different shape contours.This method directly uses vector coordinates as model input without constructing complex shape coding and considers the original contour features of building shapes,which can be applied efficiently to shape retrieval and other scenarios.Experiments show that CTW algorithm is invariant to translation,rotation,scaling and mirroring when used to measure the similarity of geometric objects,and can effectively measure the shape similarity between building shapes.The results are consistent with human spatial visual cognition.
作者
李精忠
毛凯楠
LI Jingzhong;MAO Kainan(Faculty Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen 518000,China)
出处
《测绘学报》
EI
CSCD
北大核心
2023年第12期2197-2208,共12页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(42271454,42001402)
湖北省自然科学基金(2022CFB053)
自然资源部城市国土资源监测与仿真重点实验室开放基金(KF-2022-07-017)
兰州交通大学研究生教育教学质量提升工程(JG202301)
中国科学院数字地球重点实验室开放基金(2022LDE004)。
关键词
建筑物
形状相似性
典型时间规整
空间认知
buildings
shape similarity
canonical time warping
spatial cognition