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EnglishLearning and recognizing activity in videos is an especially important task in computer vision. However, it is hard to perform. In this paper, we propose a new method by combining local and global context information to extract a bag-of-wordslike representation of a single space-time point. Each spacetime point is described by a bag of visual words that encodes its relationships with the remaining space-time points in the video, defining the space-time context. Experiments on the KTH benchmark of action recognition, show that our approach performs accurately compared to the state-of-the-art.
| Titolo: | An augmented representation of activitiy in video using semantic-context information |
| Autori: | |
| Data di pubblicazione: | 2014 |
| Abstract: | Learning and recognizing activity in videos is an especially important task in computer vision. However, it is hard to perform. In this paper, we propose a new method by combining local and global context information to extract a bag-of-wordslike representation of a single space-time point. Each spacetime point is described by a bag of visual words that encodes its relationships with the remaining space-time points in the video, defining the space-time context. Experiments on the KTH benchmark of action recognition, show that our approach performs accurately compared to the state-of-the-art. |
| Handle: | http://hdl.handle.net/11562/763761 |
| Appare nelle tipologie: | 04.01 Contributo in atti di convegno |
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