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Erschienen in:
Symmetrizing k-nn and Mutual k-nn Smoothers Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Nonparametric Classification Algorithm Based on Optimized Templates

verfasst von : J. Kalina

Erschienen in: Nonparametric Statistics

Verlag: Springer International Publishing

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Abstract

This contribution is devoted to a classification problem into two groups. A novel algorithm is proposed, which is based on a distance of each observation from the centroid (prototype, template) of one of the groups. The general procedure is described on the particular task of mouth localization in facial images, where the centroid has the form of a mouth template. While templates are most commonly constructed as simple averages of positive examples, the novel optimization criterion allows to improve the separation between observations of one group (images of mouths) and observations of the other group (images of non-mouths). The separation is measured by means of the weighted Pearson product-moment correlation coefficient. On the whole, the new classification method can be described as conceptually simple and at the same time powerful.

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Vorheriges Kapitel Constructing Confidence Sets for the Matrix Completion Problem
Nächstes Kapitel PAC-Bayesian Aggregation of Affine Estimators
Literatur
1.
Böhringer, S., Vollmar, T., Tasse, C., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., et al. (2006). Syndrome identification based on 2D analysis software. European Journal of Human Genetics, 14, 1082–1089.
2.
Chen, J. H., Chen, C. S., & Chen, Y. S. (2003). Fast algorithm for robust template matching with M-estimators. IEEE Transactions on Signal Processing, 51, 230–243.
3.
Chong, H. Y., Gortler, S. J., & Zickler, T. (2008). A perception-based color space for illumination-invariant image processing. ACM Transactions on Graphics, 27, Article 61.
4.
Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2005 (pp. 886–893).
5.
Damas, S., Cordón, O., Ibáñez, O., Santamaria, J., Alemán, I., Botella, M., & Navarro, F. (2011). Forensic identification by computer-aided craniofacial superimposition: A survey. ACM Computing Survey, 43, Article 27.
6.
Gao, J., & Hitchcock, D. B. (2010). James-Stein shrinkage to improve k-means cluster analysis. Computational Statistics & Data Analysis, 54, 2113–2127.
7.
Girosi, F., Jones, M., & Poggio, T. (1995). Regularization theory and neural networks architectures. Neural Computation 7, 219–269.
8.
Grenander, U. (1993). General pattern theory. A mathematical study of regular structures. Oxford: Oxford University Press.
9.
Guo, Y., Hastie, T., & Tibshirani, R. (2007). Regularized discriminant analysis and its application in microarrays. Biostatistics, 8, 86–100.
10.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. (2nd ed.) New York: Springer.
11.
Kalina, J. (2012). Facial symmetry in robust anthropometrics. Journal of Forensic Sciences, 57(3), 691–698.
12.
Kalina, J. (2012). Highly robust statistical methods in medical image analysis. Biocybernetics and Biomedical Engineering, 32(2), 3–16.
13.
Kalina, J. (2015). Three contributions to robust regression diagnostics. Journal of Applied Mathematics, Statistics and Informatics, 11(2), 69–78.
14.
Kalina, J., & Schlenker, A. (2015). A robust supervised variable selection for noisy high-dimensional data. BioMed Research International, 2015, Article 320385, 1–10.
15.
Shevlyakov, G. L., & Oja, H. (2016). Robust correlation: Theory and applications. New York: Wiley.
16.
Stein, C. (1956). Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 197–206). Berkeley: University of California Press.
17.
Tang, F., & Tao, H. (2007). Fast multi-scale template matching using binary features. In IEEE Workshop on Applications of Computer Vision WACV’07, 36.
18.
Tibshirani, R., Hastie, T., & Narasimhan, B. (2003). Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science, 18, 104–117.
19.
Torralba, A., Murphy, K. P., & Freeman, W. T. (2007). Sharing visual features for multiclass and multiview object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 854–869.
20.
Tuzel, O., Porikli, F., & Meer, P. (2007). Human detection via classification on Riemannian manifolds. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2007 (pp. 1–8).
21.
Vanderbei, R. J. (2009). Linear programming: Foundations and extensions (3rd ed.). New York: Springer.
22.
Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57, 137–154.
23.
Wang, C., Tong, T., Cao, L., & Miao, B. (2014). Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices. Journal of Multivariate Analysis, 125, 222–232.
24.
Wang, X., & Tang, X. (2005). Subspace analysis using random mixture models. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2005 (pp. 574–580).
25.
Wei, L., Yu, W., & Li, M. (2011). Skull assembly and completion using template-based surface matching. In Proceedings International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission 3DIMPVT 2011 (pp. 413–420).
26.
Yang, J., Han, F., Irizarry, R. A., & Liu, H. (2014). Context aware group nearest shrunken centroids in large-scale genomic studies. Journal of Machine Learning Research, 33, 1051–1059.
27.
Zelditch, M., Swiderski, D., Sheets, D. H., & Fink, W. (2012). Geometric morphometrics for biologists (2nd ed.). London: Elsevier.
28.
Zhu, X., & Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In IEEE Conference on Computer Vision and Pattern Recognition 2012 (pp. 2879–2886). New York: IEEE.
Metadaten
Titel
A Nonparametric Classification Algorithm Based on Optimized Templates
verfasst von
J. Kalina
Copyright-Jahr
2018
Buch
Nonparametric Statistics

Print ISBN: 978-3-319-96940-4

Electronic ISBN: 978-3-319-96941-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-96941-1_8