A Video Summarization Method Based on Spectral Clustering

The constant increase in the availability of digital videos has demanded the development of techniques capable of managing these data in a faster and more efficient way, especially concerning the content analysis. One of the research areas that have recently evolved significantly at this point is video summarization, which consists of generating a short version of a certain video, such that the users can grasp the central message transmitted by the original video. Many of the video summarization approaches make use of clustering algorithms, with the goal of extracting the most important frames of the videos to compose the final summary. However, special clustering algorithms based on a spectral approach have obtained superior results than those obtained with classical clustering algorithms, not only in video summarization techniques but also in other fields, such as machine learning, pattern recognition, and data mining. This work proposes a method for summarization of videos, regardless of their genre, using spectral clustering algorithms. Possibilities of algorithm parallelization for the purpose of optimizing the general performance of the proposed methodology are also discussed.