Abstract
This paper introduces a new measure for computing the similarity among 3D objects as the average volume of the largest sub-cubes matching in the objects. The match is approximate and only verified within a neighbourhood from the position of the sub-cubes. Preliminary tests performed on random and synthetic datasets prove the efficacy of the similarity measure in capturing the visual similarity among the 3D objects and a reduction in the execution time when the neighbourhood is considered.
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This work was performed during the student internship of the first author at DIMES, University of Calabria.
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Franco, F., Amelio, A., Greco, S. (2020). 3D Average Common Submatrix Measure. In: Ceci, M., Ferilli, S., Poggi, A. (eds) Digital Libraries: The Era of Big Data and Data Science. IRCDL 2020. Communications in Computer and Information Science, vol 1177. Springer, Cham. https://doi.org/10.1007/978-3-030-39905-4_4
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DOI: https://doi.org/10.1007/978-3-030-39905-4_4
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