Feature fusion-based video summarization using SegNetSN

This paper addresses the video summarization problem. For the given video goal is to find the subset of frames that capture the important events of the input video and produce a small concise summary. We formulate video summarization as a sequence labeling problem, where for a given input video a subset of frames are selected as a summary video. Based on the principle of semantic segmentation, here each pixel within a frame is assigned to one of the labels, where each frame is assigned a binary label indicating whether it will be included in the summary video or not. We propose a SegNet sequence network (SegNetSN) for video summarization and further extend the work by applying various feature fusion techniques to enhance the input. We performed experiments on the benchmark dataset TVSum.