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Video emotion recognition based on Convolutional Neural Networks
| Content Provider | Scilit |
|---|---|
| Author | Li, Chen Shi, Yuliang Yi, Xianjin |
| Copyright Year | 2021 |
| Description | Journal: Journal of Physics: Conference Series The existing video sentiment analysis methods only obtain features from the spatial and temporal signals of the video for sentiment classification, and cannot solve the difficulty of not knowing which emotion contributes the most to the entire video sentiment analysis in the video sentiment analysis. To solve this problem, a neural network with video frame weight vector is proposed. First, the video frame feature is obtained through the reel neural network, and then the weight vector layer is used to calculate the weight of the feature, and finally the frame feature with weight is put into the LSTM Training to obtain a video sentiment analysis model. We verified on the BAUM-1s data set. The results show that this method is better than existing methods in accuracy. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1738/1/012129/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1738/1/012129 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1738 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Hardware and Architecture Video Sentiment Analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
