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| 论文题目 |
Classification of Underwater Still Objects Based on Multi-field Features And SVM |
| 论文题目（英文） |
Classification of Underwater Still Objects Based on Multi-field Features And SVM |
| 作者 |
田杰 |
| 发表年度 |
2007 |
| 卷 |
6 |
| 期 |
1 |
| 页码 |
36-40 |
| 期刊名称 |
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| 摘要 |
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| 摘要_英文 |
A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two. |
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