Multimedia & Applications

Biography

Biography: Changyu Liu

Abstract

Along with the advent of big data era, available multimedia collections are expanding. To meet increasingly diversified demands of multimedia applications from the public, effective multimedia analysis approaches are required urgently. Multimedia event detection, as an emerging branch in multimedia analysis, is gaining considerable attention from both industrial and academic researchers. However, much current effort on multimedia event detection has been dedicate to detecting complex events in controlled video clips or simple events in uncontrolled video clips. In order to perform complex event detection tasks in uncontrolled video clips, we propose an event bank descriptor approach, which has been published in the journal of Neurocomputing, for multimedia representation. The approach divides spatial temporal objects of an event into objects described by a latent group logistic regression mixture model trained on a large number of labeled images which can be obtained very easily from standard image datasets, and spatial temporal relationships described by spatial temporal grids trained on a relatively small number of labeled videos which can be also obtained very easily from standard video datasets. Furthermore, we combine the coordinate descent approach and the gradient descent approach to develop an efficient iterative training algorithm to learn model parameters in the event bank descriptor, and conduct extensive experiments on the ImageNet challenge 2012 dataset and the TRECVID MED 2012 dataset. The results showed that the proposed approach outperformed state-of-the-art approaches for complex event detection in uncontrolled video clips. The benefits of our approach are mainly threefold: Firstly, outliers in training examples are removed. Secondly, subtle structural variations are allowed for detection. Thirdly, feature vectors of event bank are jointly sparse.