Volume 20, Issue 2 (12-2021)                   JIRSS 2021, 20(2): 29-42 | Back to browse issues page


XML Print


Department of Statistics, Amirkabir University of Technology (Tehran Polytechnic). , adel@aut.ac.ir
Abstract:   (553 Views)

Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and speeds up the shape clustering algorithms. Although the mentioned method is not very accurate, it is fast; therefore, it is useful for datasets with a high number of landmarks or observations, which take a long time to be clustered by means of other algorithms. It should be noted that this method is not new, but in this article we apply it in shape data analysis.

Full-Text [PDF 186 kb]   (669 Downloads)    
Type of Study: Review Article | Subject: 62Hxx: Multivariate analysis
Received: 2020/11/6 | Accepted: 2021/11/9 | Published: 2022/04/12

References
1. Amaral, G. J. A., Dore, L. H., Lessa, R. P., Stosic, B. (2010), K-means algorithm in statistical shape analysis. Communications in Statistics-Simulation and Computation, 39(5), 1016-1026. [DOI:10.1080/03610911003765777]
2. Dryden, I. L. and Mardia, K. V. (2016), Statistical Shape Analysis: with Applications in R. 2nd ed.. Wiley, Chichester. [DOI:10.1002/9781119072492]
3. Gan, G, Ma, C, and Wu, J. (2007), Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability. [DOI:10.1137/1.9780898718348]
4. Gonzalez R. C. and Woods R. E. (2002), Digital Image Processing. Prentice Hall.
5. Ishihara, S., Ishihara, K., and Nagamachi, M. (2011), Statistical shape of Head-lights. IEEE International Conference on Biometrics and Kansei Engineering, 27-32. [DOI:10.1109/ICBAKE.2011.52]
6. Izenman, A. J. (2008), Modern Multivariate Statistical Techniques. Springer, New York. [DOI:10.1007/978-0-387-78189-1]
7. Kendall, D. G. (1977), The diffusion of shape. Advances in Applied Probability, 9, 428-430. [DOI:10.2307/1426091]
8. Lele, S. R. and Richtsmeier, J. T. (2001), An Invariant Approach to Statistical Analysis of Shapes. Chapman and Hall/CRC, Boca Raton. [DOI:10.1201/9781420036176]
9. Mohammadpour, A., FĂ©ron, O., and Mohammad-Djafari A. (2004), Bayesian segmentation of hyperspectral images. Bayesian Inference and Maximum Entropy Methods, Maxent Workshop, Jul. pp. 541-548. [DOI:10.1063/1.1835254]
10. Nabil, M. and Golalizadeh, M. (2016), On clustering shape data. Journal of Statistical Computation and Simulation, 86, 2995-3008. [DOI:10.1080/00949655.2016.1144754]
11. Rencher, A. C. (2002), Methods of Multivariate Analysis. Wiley. [DOI:10.1002/0471271357]
12. Srivastava, A., Joshi, S. H., Mio, W., and Liu, X. (2005), Statistical shape analysis: Clustering, learning, and testing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4): 590-602. [DOI:10.1109/TPAMI.2005.86]

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.