Classification of underwater still objects based on multi-field features and SVM
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 ti...
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| Veröffentlicht in: | Journal of marine science and application 2007-03, Vol.6 (1), p.36-40 |
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| container_title | Journal of marine science and application |
| container_volume | 6 |
| creator | Tian, Jie Xue, Shan-hua Huang, Hai-ning Zhang, Chun-hua |
| description | 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. |
| doi_str_mv | 10.1007/s11804-007-6042-4 |
| format | Article |
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| fulltext | fulltext |
| identifier | ISSN: 1671-9433 |
| ispartof | Journal of marine science and application, 2007-03, Vol.6 (1), p.36-40 |
| issn | 1671-9433 1993-5048 |
| language | eng |
| recordid | cdi_wanfang_journals_hebgcdxxb_e200701006 |
| source | Alma/SFX Local Collection; SpringerLink Journals - AutoHoldings |
| subjects | 多场特征提取 支持向量机 水下静止目标 |
| title | Classification of underwater still objects based on multi-field features and SVM |
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