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Feature Extraction Using Histogram Entropies of Euclidean Distances for Vehicle Classification

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5 Author(s)
Ming Bao ; Institute of Acoustics, Chinese Academy of Sciences, Beijing 100080, China. ; Luyang Guan ; Xiaodong Li ; Jing Tian
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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 (Jrd ) 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

Published in:

2006 International Conference on Computational Intelligence and Security  (Volume:1 )

Date of Conference:

Nov. 2006