Static video summarization based on genetic algorithm and deep learning approach

The development of information technology has led to the rise of big data. A large portion of this big data comes in the form of video information. The automatic analysis of this exponential growth in video content has become a popular research area. This research focuses on finding a video’s keyframes through a proposed static video summarization method. The method uses a deep learning-based shot boundary detection approach as a pre-processing step and exploits DBSCAN clustering to extract keyframes. A genetic algorithm is used to optimize the hyper-parameters of DBSCAN rather than having the user pre-tune them because the number of keyframes in a video can vary depending on the content of the video. The experimental results on standard databases Open Video Project (OVP) and YouTube (YT) show that the proposed method produces better results than existing methods.