| Abstract: | 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. |
