Video Event Detection Using Kernel Support Vector Machine with Isotropic Gaussian Sample Uncertainty (KSVM-iGSU)

Christos Tzelepis, Vasileios Mezaris, Ioannis Patras

Abstract: In this paper, we propose an algorithm that learns from uncertain data and exploits related videos for the problem of event detection; related videos are those that are closely associated, though not fully depicting the event of interest. In particular, two extensions of the linear SVM with Gaussian Sample Uncertainty are presented, which (a) lead to non-linear decision boundaries and (b) incorporate related class samples in the optimization problem. The resulting learning methods are especially useful in problems where only a limited number of positive and related training observations are provided, e.g., for the 10Ex subtask of TRECVID MED, where only ten positive and five related samples are provided for the training of a complex event detector. Experimental results on the TRECVID MED 2014 dataset verify the effectiveness of the proposed methods.