Name
Title | ||
|---|---|---|
Hybrid convolutional neural networks and optical flow for video visual attention prediction. |
Abstract | ||
|---|---|---|
In this paper, a convolutional neural networks (CNN) and optical flow based method is proposed for prediction of visual attention in the videos. First, a deep-learning framework is employed to extract spatial features in frames to replace those commonly used handcrafted features. The optical flow is calculated to obtain the temporal feature of the moving objects in video frames, which always draw audiences’ attentions. By integrating these two groups of features, a hybrid spatial temporal feature set is obtained and taken as the input of a support vector machine (SVM) to predict the degree of visual attention. Finally, two publicly available video datasets were used to test the performance of the proposed model, where the results have demonstrated the efficacy of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-018-5793-z | Multimedia Tools Appl. |
Keywords | Field | DocType |
Convolutional neural networks, Optical flow, Spatial temporal feature, Visual attention | Computer vision,Pattern recognition,Computer science,Convolutional neural network,Support vector machine,Feature set,Visual attention,Artificial intelligence,Optical flow | Journal |
Volume | Issue | ISSN |
77 | 22 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 40 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meijun Sun | 1 | 74 | 11.77 |
| Ziqi Zhou | 2 | 5 | 1.76 |
| Dong Zhang | 3 | 51 | 2.03 |
| Zheng Wang | 4 | 43 | 4.79 |