Abstract
This paper presents a novel method for feature extraction based on the generalized entropy of the histogram formed by Euclidean distances, which is named distributive entropy of Euclidean distance (DEED in sort). DEED is a nonlinear measure for learning feature space, which provides the congregate and information measure of learning samples space. The ratio of between-class DEED to within-class DEED (J rd ) is used as a new nonlinear separability criterion for optimizing feature selection. Experiments on vehicle classification show that the proposed method has better performance on all the datasets than the fisher linear discriminant analysis.
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© 2007 Springer-Verlag Berlin Heidelberg
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Bao, M., Guan, L., Li, X., Tian, J., Yang, J. (2007). Feature Extraction Using Histogram Entropies of Euclidean Distances for Vehicle Classification. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_114
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DOI: https://doi.org/10.1007/978-3-540-74377-4_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74376-7
Online ISBN: 978-3-540-74377-4
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