Enhancement of perceptual quality in static video summarization using minimal spanning tree approach

A video summarization technique is proposed in this work using minimal spanning tree (MST) of data points. The data points correspond to image frames of a shot in the video which is to be summarized. Correlation is chosen as a similarity metric for computing the edge weights of the MST. The representative frames for each shot are chosen by computing the density of each data point. A novel method for redundancy reduction is devised using SURF and GIST. The redundant frames are eliminated for concise representation of the video. The degree of reduction achieved by using the two approaches is also presented. The proposed method is assessed for perceptual quality against manual summarization. High values of precision and recall endorse the efficacy of the method. The system works well for different kinds of video without a priori knowledge about the type or content of it. Two datasets are considered for experimentation, one comprises short videos while the other consists of long videos. The method is found to provide satisfactory results for both the datasets.