Nonlinear kernel sparse dictionary selection for video summarization

Sparse dictionary selection (SDS) has demonstrated to be an effective solution for keyframe based video summarization (VS), which generally assumes a linear relation among similar video frames. However, such a linear assumption is not always true for videos. In this paper, the nonlinearity among frames is taken into consideration and a nonlinear SDS model is formulated for VS, in which the nonlinearity is transformed to linearity by projecting a video to a high dimensional feature space induced by a kernel function. Moreover, a kernel simultaneous orthogonal matching pursuit (KSOMP) is proposed to solve the problem. In order to achieve an intuitive and flexible configuration of the VS process, an adaptive criterion is devised to produce video summaries with different lengths for different video content. Experimental results on benchmark video datasets demonstrate that the proposed algorithm outperforms several state-of-the-art VS algorithms.