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

Author

Listed:
  • 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
    Department of Mining Exploitation and Prospecting, Universidad de Oviedo, 33004 Oviedo, Spain)

  • 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)

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.

Suggested Citation

  • Manuel Oviedo-de la Fuente & Carlos Cabo & Celestino Ordóñez & Javier Roca-Pardiñas, 2021. "A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification," Mathematics, MDPI, vol. 9(12), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1328-:d:571594
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    References listed on IDEAS

    as
    1. Zelen, Marvin, 1991. "Multinomial response models," Computational Statistics & Data Analysis, Elsevier, vol. 12(2), pages 249-254, September.
    2. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    3. Bommert, Andrea & Sun, Xudong & Bischl, Bernd & Rahnenführer, Jörg & Lang, Michel, 2020. "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
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    Cited by:

    1. Manuel Oviedo de la Fuente & Carlos Cabo & Javier Roca-Pardiñas & E. Louise Loudermilk & Celestino Ordóñez, 2024. "3D Point Cloud Semantic Segmentation Through Functional Data Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 723-744, December.

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