基于L_1范数的形状快速匹配算法

Fast shape retrieval based on L_1 norm

针对内距离形状上下文(inner-distance shape context,IDSC)和轮廓点分布直方图(contours points distribution histogram,CPDH)在形状相似性度量中直方图匹配耗时长、工程应用性不佳的问题,提出了一种用EMD-L_1测量轮廓特征直方图距离的方法。EMD-L_1在原始EMD(earth mover’s distance)的基础上融合了L_1范数,通过替换地面距离计算方法,减少了目标函数的变量,加快了直方图匹配的速度,能够快速实现形状匹配并保持较好的检索性能。对形状数据集进行仿真实验的结果证明,该方法能够有效地进行数据集的形状识别和检索,并且在MNIST数据集下的匹配速度优于其他算法。

In order to solve the problem that the histogram matching time is long and the engineering application is poor,this paper proposed a method that using EMD-L1 to measure the distance between two feature histograms. EMD-L1 fusioned the L1 norm based on the original EMD and replace the calculation of the ground distance to reduce the number of unknown variables.It achieves shape matching quickly and has a good retrieval performance. With a great deal of experiments in several shape databases,the results show that the performance of novel method is superior to original algorithm. And the matching speed is better than other algorithms under the MNIST data set.