Measuring similarity for query in geometric data

One of the most important steps which is used in every data mining projects is searching an object or some similar objects in a data set. For geometric data, there are some methods that measure the difference between two objects. In recent years, researchers have focused on these types of metrics and used them in different applications (e.g., shape matching, machine vision, map generation, etc.). The query problem in these kinds of applications is more complicated when we have big data. In this paper, a new metric is presented which works efficiently when the geometric objects are in discrete form (e.g., polygon or chain). The presented method is important from a theoretical point of view, and its differences with other similar metrics are discussed in this paper.