目的 全局描述不变量具有低维数和相似性测量简单的特点，针对非规则弯曲形变图形，提出一种基于中轴骨架测地距离改进的全局描述不变量。方法 首先利用测地距离对于弯曲形变的不敏感性，修正图像质心位置；其次提取弯曲形变图像的中轴骨架代替图像全局域作为运算点集，并分配权重，降低计算复杂度，进而以此为基础提出一种单维度全局不变量。结果 实验结果表明该不变量具有良好的TRS（translation，rotation，scaling）不变性和弯曲形变不敏感性，相对其他传统特征表示方法，能够有效表示和鉴别非规则弯曲图形。结论 本文提出的单维度全局描述不变量，结构简单，计算方便。对于同类图像的弯曲变形，能够有效地进行区分，为该类图形的识别提供一种新的方法和思路。

Objective Global descriptors have the future of low dimension and simple similarity measurement.An improved global invariant is proposed based on the medial axis skeletons and geodesic distance for bending deformation.Method First, we use the geodesic distance to correct the image centroid, and then we extract the skeleton instead of the global image domain as operational point set to reduce the computational complexity. Finally, we propose single dimension invariants.Result The experimental results show that compared with other traditional feature representation the invariant is bending insensitive and meets the test of TRS(translation,rotation,scaling) invariance.It can effectively represent and identify bending non-rigid shapes.Conclusion In this paper, we propose a novel global invariant of single dimension.Its simple structure makes it possible to use simple calculations to achieve real-time performance.This descriptor provides a new method to robustly identify objects with bending deformation.