Feature extraction using histogram entropies of euclidean distances for vehicle classification

Ming Bao; Luyang Guan; Xiaodong Li; Jing Tian; Jun Yang

doi:10.1109/ICCIAS.2006.294219

Feature extraction using histogram entropies of euclidean distances for vehicle classification

Ming Bao^*, Luyang Guan, Xiaodong Li, Jing Tian, Jun Yang

^*Corresponding author for this work

CAS - Institute of Acoustics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Publisher	IEEE Computer Society
Pages	668-673
Number of pages	6
ISBN (Print)	1424406056, 9781424406050
DOIs	https://doi.org/10.1109/ICCIAS.2006.294219
Publication status	Published - 2006
Externally published	Yes
Event	2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 - Guangzhou, China Duration: Oct 3 2006 → Oct 6 2006

Publication series

Name	2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Volume	1

Conference

Conference	2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Country/Territory	China
City	Guangzhou
Period	10/3/06 → 10/6/06

ASJC Scopus Subject Areas

General Computer Science
Control and Systems Engineering

Access to Document

10.1109/ICCIAS.2006.294219

Cite this

Bao, M., Guan, L., Li, X., Tian, J., & Yang, J. (2006). Feature extraction using histogram entropies of euclidean distances for vehicle classification. In 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 (pp. 668-673). Article 4072172 (2006 International Conference on Computational Intelligence and Security, ICCIAS 2006; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICCIAS.2006.294219

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title = "Feature extraction using histogram entropies of euclidean distances for vehicle classification",

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 (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.",

author = "Ming Bao and Luyang Guan and Xiaodong Li and Jing Tian and Jun Yang",

year = "2006",

doi = "10.1109/ICCIAS.2006.294219",

language = "English",

isbn = "1424406056",

series = "2006 International Conference on Computational Intelligence and Security, ICCIAS 2006",

publisher = "IEEE Computer Society",

pages = "668--673",

booktitle = "2006 International Conference on Computational Intelligence and Security, ICCIAS 2006",

address = "United States",

note = "2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 ; Conference date: 03-10-2006 Through 06-10-2006",

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Bao, M, Guan, L, Li, X, Tian, J & Yang, J 2006, Feature extraction using histogram entropies of euclidean distances for vehicle classification. in 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006., 4072172, 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006, vol. 1, IEEE Computer Society, pp. 668-673, 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006, Guangzhou, China, 10/3/06. https://doi.org/10.1109/ICCIAS.2006.294219

Feature extraction using histogram entropies of euclidean distances for vehicle classification. / Bao, Ming; Guan, Luyang; Li, Xiaodong et al.
2006 International Conference on Computational Intelligence and Security, ICCIAS 2006. IEEE Computer Society, 2006. p. 668-673 4072172 (2006 International Conference on Computational Intelligence and Security, ICCIAS 2006; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Feature extraction using histogram entropies of euclidean distances for vehicle classification

AU - Bao, Ming

AU - Guan, Luyang

AU - Li, Xiaodong

AU - Tian, Jing

AU - Yang, Jun

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

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DO - 10.1109/ICCIAS.2006.294219

M3 - Conference contribution

AN - SCOPUS:38549096506

SN - 1424406056

SN - 9781424406050

T3 - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006

SP - 668

EP - 673

BT - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006

PB - IEEE Computer Society

T2 - 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006

Y2 - 3 October 2006 through 6 October 2006

ER -

Bao M, Guan L, Li X, Tian J, Yang J. Feature extraction using histogram entropies of euclidean distances for vehicle classification. In 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006. IEEE Computer Society. 2006. p. 668-673. 4072172. (2006 International Conference on Computational Intelligence and Security, ICCIAS 2006). doi: 10.1109/ICCIAS.2006.294219

Feature extraction using histogram entropies of euclidean distances for vehicle classification

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