Abstract
Measuring difference or similarity between data is one of the most fundamental steps in data science. This topic is of the utmost importance in many artificial intelligent systems, machine learning and any data mining and knowledge extraction. There are several applications in image processing, map analysis, self-driving cars, GIS, etc., when data are in the shape of polygons or chains. In the present study, principal metrics for comparing geometric data are studied. In each metric, one, two or three features out of similarity, scaling, and spatial distance is considered. Evaluation for metrics based on three perspectives is discussed and results are provided in a detailed table. Additionally, for each case, one practical application is presented.
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Bigham, B.S., Mazaheri, S. (2020). A Survey on Measurement Metrics for Shape Matching Based on Similarity, Scaling and Spatial Distance. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_2
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DOI: https://doi.org/10.1007/978-3-030-37309-2_2
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