Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network
With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been hig...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/8188936 |
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| _version_ | 1832552769828945920 |
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| author | Jie Liu Jiamin Zhang |
| author_facet | Jie Liu Jiamin Zhang |
| author_sort | Jie Liu |
| collection | DOAJ |
| description | With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks. |
| format | Article |
| id | doaj-art-4f3417c20a49401089e6434aac740473 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-4f3417c20a49401089e6434aac7404732025-02-03T05:57:54ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/8188936Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural NetworkJie Liu0Jiamin Zhang1Department of Computer EngineeringUniversity Medical schoolWith the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.http://dx.doi.org/10.1155/2022/8188936 |
| spellingShingle | Jie Liu Jiamin Zhang Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network Advances in Multimedia |
| title | Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network |
| title_full | Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network |
| title_fullStr | Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network |
| title_full_unstemmed | Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network |
| title_short | Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network |
| title_sort | target recognition technology of multimedia platform based on a convolutional neural network |
| url | http://dx.doi.org/10.1155/2022/8188936 |
| work_keys_str_mv | AT jieliu targetrecognitiontechnologyofmultimediaplatformbasedonaconvolutionalneuralnetwork AT jiaminzhang targetrecognitiontechnologyofmultimediaplatformbasedonaconvolutionalneuralnetwork |