A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Manuel Oviedo- de la Fuente, Carlos Cabo, Celestino Ordóñez and Javier Roca-Pardiñas
Additional contact information
Manuel Oviedo- de la Fuente: CITIC, Research Group MODES, Department of Mathematics, Universidade da Coruña, 15001 Coruña, Spain
Carlos Cabo: Faculty of Science and Engineering, Swansea University, Swansea SA2 8PP, UK
Celestino Ordóñez: Department of Mining Exploitation and Prospecting, Universidad de Oviedo, 33004 Oviedo, Spain
Javier Roca-Pardiñas: Department of Statistics and OR, SiDOR Research Group & CINBIO, Universidade de Vigo, 36310 Vigo, Spain

Mathematics, 2021, vol. 9, issue 12, 1-19

Abstract: Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.

Keywords: 3D point clouds; multiclass classification; feature selection; distance correlation; functional data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/12/1328/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/12/1328/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:12:p:1328-:d:571594

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().