期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 75, 期 15, 页码 9045-9072出版社
SPRINGER
DOI: 10.1007/s11042-015-2819-7
关键词
Capturing content variation in time in video; Modified vector of locally aggregated descriptor; Random forests; Video classification
类别
资金
- Sectoral Operational Programme Human Resources Development of the Ministry of European Funds through the Financial Agreement [POS-DRU/159/1.5/S/132395]
In order to reduce the computational complexity, most of the video classification approaches represent video data at frame level. In this paper we investigate a novel perspective that combines frame features to create a global descriptor. The main contributions are: (i) a fast algorithm to densely extract global frame features which are easier and faster to compute than spatio-temporal local features; (ii) replacing the traditional k-means visual vocabulary from Bag-of-Words with a Random Forest approach allowing a significant speedup; (iii) the use of a modified Vector of Locally Aggregated Descriptor(VLAD) combined with a Fisher kernel approach that replace the classic Bag-of-Words approach, allowing us to achieve high accuracy. By doing so, the proposed approach combines the frame-based features effectively capturing video content variation in time. We show that our framework is highly general and is not dependent on a particular type of descriptors. Experiments performed on four different scenarios: movie genre classification, human action recognition, daily activity recognition and violence scene classification, show the superiority of the proposed approach compared to the state of the art.
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