Name
Title | ||
|---|---|---|
Network video summarization based on key frame extraction via superpixel segmentation |
Abstract | ||
|---|---|---|
The spread of insecure online video has been a serious social problem. The video summarization becomes one of key step for automatic filtering the expected video from the Internet. At present, the most existing video summarization methods are based on calculating the image similarity between video frames, so that the key frame can be selected properly. In this article, we introduce a superpixel segmentation based image similarity calculation, and then the metric is applied into video summarization. To identify the video key frames, we introduce superpixel segmentation to cluster the pixels locally by estimating the optical flow displacement field between successive frames, which can extract key frames and reduce video redundancy. On the VSUMM dataset and YouTube dataset, the experimental results demonstrate that the proposed method has clear advantages on both subjectively qualitative analysis and objectively quantitative evaluation comparing with the state of the art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/ett.3940 | TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES |
DocType | Volume | Issue |
Journal | 33 | 6 |
ISSN | Citations | PageRank |
2161-3915 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Jin | 1 | 19 | 11.49 |
| Yang Yu | 2 | 94 | 55.24 |
| Yumeng Li | 3 | 0 | 0.34 |
| Zhaolin Xiao | 4 | 4 | 5.15 |