Video event recounting using mixture subclass discriminant analysis

In this paper, a new feature selection method is used, in combination with a semantic model vector video representation, in order to enumerate the key semantic evidences of an event in a video signal. In particular, a set of semantic concept detectors is firstly used for estimating a model vector for each video signal, where each element of the model vector denotes the degree of confidence that the respective concept is depicted in the video. Then, a novel feature selection method is learned for each event of interest. This method is based on exploiting the first two eigenvectors derived using the eigenvalue formulation of the mixture subclass discriminant analysis. Subsequently, given a video-event pair, the proposed method jointly evaluates the significance of each concept for the detection of the given event and the degree of confidence with which this concept is detected in the given video, in order to decide which concepts provide the strongest evidence in support of the provided video-event link. Experimental results using a video collection of TRECVID demonstrate the effectiveness of the proposed video event recounting method.