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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15844 |
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| _version_ | 1866910581838053376 |
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| author | Zhang, Xueqing Fu, Di Liu, Naihao |
| author_facet | Zhang, Xueqing Fu, Di Liu, Naihao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Shot Segmentation Based on Von Neumann Entropy for Key Frame Extraction Zhang, Xueqing Fu, Di Liu, Naihao Computer Vision and Pattern Recognition Information Theory 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. |
| title | Shot Segmentation Based on Von Neumann Entropy for Key Frame Extraction |
| topic | Computer Vision and Pattern Recognition Information Theory |
| url | https://arxiv.org/abs/2408.15844 |