Shot Segmentation Based on Von Neumann Entropy for Key Frame Extraction
This work addresses video summarization and retrieval for applications like compression, but it appears incremental as it builds on existing shot segmentation techniques with a specific entropy measure.
The authors tackled video key frame extraction by proposing a shot segmentation method using Von Neumann entropy to compute frame similarities, selecting initial frames of shots as key frames, resulting in accurate representation of video content with minimized repeated frames.
Video key frame extraction is important in various fields, such as video summary, retrieval, and compression. Therefore, we suggest a video key frame extraction algorithm based on shot segmentation using Von Neumann entropy. The segmentation of shots is achieved through the computation of Von Neumann entropy of the similarity matrix among frames within the video sequence. The initial frame of each shot is selected as key frames, which combines the temporal sequence information of frames. The experimental results show the extracted key frames can fully and accurately represent the original video content while minimizing the number of repeated frames.