|
|
|
| 论文题目 |
Feature Extraction Using Histogram Entropies of Euclidean Distances for Vehicle Classification |
| 论文题目（英文） |
Feature Extraction Using Histogram Entropies of Euclidean Distances for Vehicle Classification |
| 作者 |
鲍明 |
| 发表年度 |
2007 |
| 卷 |
4456 |
| 期 |
4 |
| 页码 |
1085-1096 |
| 期刊名称 |
|
| 摘要 |
|
| 摘要_英文 |
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{sub}(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. |
|
|
|
