High-dimensional multimedia classification using deep CNN and extended residual units
文献类型：期刊论文
| 作者 | Shamsolmoali, Pourya2; Jain, Deepak Kumar3; Zareapoor, Masoumeh2; Yang, Jie2 ; Alam, M. Afshar1
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| 刊名 | MULTIMEDIA TOOLS AND APPLICATIONS
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 78期号:17页码:23867-23882 |
| 关键词 | High dimensional Multimedia data classification Deep learning Feature extraction Residual network |
| ISSN号 | 1380-7501 |
| DOI | 10.1007/s11042-018-6146-7 |
| 通讯作者 | Shamsolmoali, Pourya(pshams55@gmail.com) |
| 英文摘要 | Processing multimedia data has emerged as a key area for the application of machine learning methods Building a robust classification model to use in high dimensional space requires the combination of a deep feature extractor and a powerful classifier. We present a new classification pipeline to facilitate multimedia data analysis based on convolutional neural network and the modified residual network which can integrate with the other feedforward network style in an endwise training fashion. The proposed residual network is producing attention-aware features. We proposed a unified deep CNN model to achieve promising performance in classifying high dimensional multimedia data by getting the advantages of the residual network. In every residual module, up-down and vice-versa feedforward structure is implemented to unfold the feedforward and backward process into a unique process. The hybrid proposed model evaluated on four datasets and have been shown promising results which outperform the previous best results. Last but not the least, the proposed model achieves detection speeds that are much faster than other approaches. |
| WOS关键词 | FEATURE-SELECTION ; REPRESENTATION |
| 资助项目 | NSFC, China[61572315] ; Committee of Science and Technology, Shanghai, China[17JC1403000] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000482419900005 |
| 出版者 | SPRINGER |
| 资助机构 | NSFC, China ; Committee of Science and Technology, Shanghai, China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27220]  |
| 专题 | 离退休人员 |
| 通讯作者 | Shamsolmoali, Pourya |
| 作者单位 | 1.Jamia Hamdard, Dept Comp Sci & Engn, New Delhi, India 2.Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shamsolmoali, Pourya,Jain, Deepak Kumar,Zareapoor, Masoumeh,et al. High-dimensional multimedia classification using deep CNN and extended residual units[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(17):23867-23882. |
| APA | Shamsolmoali, Pourya,Jain, Deepak Kumar,Zareapoor, Masoumeh,Yang, Jie,&Alam, M. Afshar.(2019).High-dimensional multimedia classification using deep CNN and extended residual units.MULTIMEDIA TOOLS AND APPLICATIONS,78(17),23867-23882. |
| MLA | Shamsolmoali, Pourya,et al."High-dimensional multimedia classification using deep CNN and extended residual units".MULTIMEDIA TOOLS AND APPLICATIONS 78.17(2019):23867-23882. |
入库方式： OAI收割
来源：自动化研究所
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