Small Sample Image Recognition Based on CNN and RBFNN
文献类型：期刊论文
| 作者 | Yao, Biyuan4; Zhou, Hui3; Yin, Jianhua2; Li, Guiqing4; Lv, Chengcai1
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| 刊名 | JOURNAL OF INTERNET TECHNOLOGY
 |
| 出版日期 | 2020 |
| 卷号 | 21期号:3页码:881-889 |
| 关键词 | Image recognition TensorFlow Fourier transform Roberts operator CNN RBFNN |
| ISSN号 | 1607-9264 |
| DOI | 10.3966/160792642020052103025 |
| 英文摘要 | Identification of dangerous goods based on images plays a key role in the security inspection of various situations such as airports, subways, public places etc. This paper discusses the issue in a from-simple-to-complex manner. Firstly, we classify different kinds of knives given an image including a single object without complex background in the framework of TensorFlow. Then, according to the color and shape features of a single image, where Fourier transform and Roberts operator is used to judge of the complex scene which doesn't contain knives from an image with natural background. Finally, convolution neural network (CNN) and radial basis function neural network (RBFNN) are used to construct identification models for images of objects in six categories. The obtained accuracy of the true and predicted values of the CNN and RBFNN are 66.67% for training on CNN and 76.67% on RBFNN, for testing 50% on CNN and 44.44% on RBFNN respectively. The results showed that the constructed of identification model is able to perform recognition for small-scale image database and reduce the false alarm rate. Furthermore, our method is robust in dealing with the small sample, with high classification accuracy and low cost. The models have few layers and nodes. |
| WOS关键词 | NEURAL-NETWORK |
| 资助项目 | National Natural Science Foundation of China[61662019] ; Natural Science Foundation of Hainan Province[117212] ; Nature Science Foundation of Guangdong Province[2017A030313347] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000540310600027 |
| 出版者 | LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Hainan Province ; Nature Science Foundation of Guangdong Province |
| 源URL | [http://ir.idsse.ac.cn/handle/183446/7768]  |
| 专题 | 深海工程技术部_深海视频技术研究室 |
| 通讯作者 | Zhou, Hui |
| 作者单位 | 1.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Beijing, Peoples R China 2.Hainan Univ, Sch Sci, Haikou, Hainan, Peoples R China 3.Hainan Univ, Sch Comp Sci & Cyberspace Secur, Haikou, Hainan, Peoples R China 4.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yao, Biyuan,Zhou, Hui,Yin, Jianhua,et al. Small Sample Image Recognition Based on CNN and RBFNN[J]. JOURNAL OF INTERNET TECHNOLOGY,2020,21(3):881-889. |
| APA | Yao, Biyuan,Zhou, Hui,Yin, Jianhua,Li, Guiqing,&Lv, Chengcai.(2020).Small Sample Image Recognition Based on CNN and RBFNN.JOURNAL OF INTERNET TECHNOLOGY,21(3),881-889. |
| MLA | Yao, Biyuan,et al."Small Sample Image Recognition Based on CNN and RBFNN".JOURNAL OF INTERNET TECHNOLOGY 21.3(2020):881-889. |
入库方式： OAI收割
来源：深海科学与工程研究所
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