图 书 馆
流式分层视频分割Streaming Hierarchical Video Segmentation |
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| 课程网址: | http://videolectures.net/eccv2012_corso_video/ |
| 主讲教师: | Bernt Schiele; David Forsyth |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2012-11-12 |
| 课程语种: | 英语 |
| 中文简介: |  尽管有许多可用的视频分割方法，但将视频分割作为视频分析的早期处理步骤却落后于将图像分割用于图像分析。造成这种滞后的主要原因是，视频比图像大一个数量级。但是大多数方法都要求将视频中的所有体素都加载到内存中，这显然对于中等长度的视频也是禁止的。我们通过提出一种近似框架来解决此限制，该框架由数据流算法驱动，用于流式分层视频分段：每个视频帧仅处理一次，并且不会更改先前帧的分段。我们在我们的流框架中实现了基于图的分层分割方法；我们的方法是第一个提出的流式分层视频分割方法。我们会对基准视频数据集和更长的视频进行全面的实验分析。结果表明，基于图的流分层方法优于其他流视频分割方法，其性能几乎与基于完整视频分层图的方法一样好。 |
| 课程简介: | The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method. |
| 关 键 词: | 视频分割; 数据流算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-29：zyk |
| 最后编审: | 2021-06-25：zyk |
| 阅读次数: | 100 |