Multimedia event detection using GMM supervectors and SVMS
Yusuke Kamishima, Nakamasa Inoue, Koichi Shinoda, Shunsuke Sato · 2012
In multimedia event detection, complex target events are extracted from a large set of consumer-generated videos taken in unconstrained environments. We devised a multimedia event detection method based on GMM supervectors and support vector machines (SVMs) using multiple features. A GMM supervector consists of the parameters of a Gaussian mixture model (GMM) for the distribution of local features extracted from a video clip. A GMM is regarded as an extension of the Bag-of-Words (BoW) to a probabilistic framework, and thus, it can be expected to be robust against the data insufficiency problem. This method outperformed previous methods including BoW in experiments using the dataset of the multimedia event detection task in TRECVID2010 and 2011.