Name
Abstract | ||
|---|---|---|
Psychology studies have shown that an image can invoke various emotions, depending on the visual features as well as semantic content of the image. Ability to identify image emotion can be very useful for many applications, including image retrieval and aesthetics prediction. Notably, most of the existing deep learning-based emotion recognition models do not capitalize on additional semantics or contextual information and are computational expensive. Inspired to overcome these limitations, we proposed a lightweight multi-stream deep network that concatenates several MobileNet networks for performing image emotion analysis. Each stream in the multi-stream deep network represents the core emotion recognition, object recognition and image category recognition models respectively. Experimental results demonstrate the effectiveness of the additional contextual information in producing comparable performance as the state-of-the-art emotion models, but with lesser parameters, thus improving its practicality. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MMSP.2019.8901699 | 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP) |
Keywords | Field | DocType |
Image emotion,lightweight,multi-stream network | Contextual information,Pattern recognition,Computer science,Emotion recognition,Image retrieval,Artificial intelligence,Natural language processing,Deep learning,Semantics,Deep neural networks,Cognitive neuroscience of visual object recognition | Conference |
ISSN | ISBN | Citations |
2163-3517 | 978-1-7281-1818-5 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Han Chew | 1 | 0 | 0.34 |
| Lai-Kuan Wong | 2 | 85 | 11.99 |
| John See | 3 | 57 | 10.86 |
| Huai-Qian Khor | 4 | 4 | 2.09 |
| Balasubramanian Abivishaq | 5 | 0 | 0.34 |