Beyond Bag-of-Words: Fast video classification with Fisher Kernel Vector of Locally Aggregated Descriptors
In this paper we introduce a new video description framework that replaces traditional Bag-of-Words with a combination of Fisher Kernels (FK) and Vector of Locally Aggregated Descriptors (VLAD). The main contributions are: (i) a fast algorithm to densely extract global frame features, easier and faster to compute than spatio-temporal local features; (ii) replacing the traditional k-means based vocabulary with a Random Forest approach that allows significant speedup; (iii) use of a modified VLAD and FK representation to replace the classic Bag-of-Words and obtaining better performance. We show that our framework is highly general and is not dependent on a particular type of descriptor. It achieves state-of-the-art results in several classification scenarios.