A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation

The continuous creation of digital video has caused an exponential growth of digital video content. To increase the usability of such large volume of videos, a lot of research has been made. Video summarization has been proposed to rapidly browse large video collections. To summarize any type of video, researchers have relied on visual features contained in frames. In order to extract these features, different techniques have used local or global descriptors. In this paper, we propose a method for static video summarization that can produce meaningful and informative video summaries. We perform an evaluation using over 100 videos in order to achieve a stronger position about the performance of local descriptors in semantic video summarization. Our experimental results show, with a confidence level of 99%, that our proposed method using local descriptors and temporal video segmentation produces better summaries than state of the art methods. We also demonstrate the importance of a more elaborate method for temporal video segmentation, improving the generation of summaries, achieving 10% improvement in accuracy. We also acknowledge a marginal importance of color information when using local descriptors to produce video summaries.