Unsupervised Video Summaries Using Multiple Features and Image Quality
It is important to generate both interesting and representative video summary for massive videos. This work proposes a new method to generate dynamic video summary using multiple features and image quality without human's involvement in the whole procedure. Specifically, we first split a video into several video clips. Second, a set of features including visual attention, exposure of light, saturation, hue, rule of thirds, contrast and directionality is computed and the qualities of video clips are also estimated. Then, the importance of each video clip is obtained based on these features and the estimated quality. Finally, based on the importance value, we sort clip values in descending order and select an optimal subset to generate video summary. Experimental results demonstrate that the proposed method enables to generate high-quality video summary.