This paper introduced algorithms for video segmentation and scene clustering.
本文介绍了实现视频分割和场景聚类的算法。
This thesis proposes a new effective scene clustering method which can automatically decide the stop point without prior knowledge.
本文提出了一种新的有效的场景聚类算法，无需先验知识，自动决定算法停止点。
Because scenes have much difference in appearance, we adopt unsupervised scene clustering for its universality, which groups video shots with similar visual content into same cluster.
对于视频来说，由于存在较大的场景差异，因此采用无监督的场景聚类，达到通用性的目的。
Our experimental results demonstrate the performance of scene change detection based on shot is better than that of shot clustering based on multi-features.
实验表明，基于镜头的场景边界检测性能优于基于多特征的镜头聚类分析。
Based on the border in the shot clustering algorithm, we proposed the method of using the camera frame color image characteristics and the overall situation of movement to the scene segmentation.
在基于镜头边界的聚类算法基础上，提出了利用镜头帧图像的全局颜色特征和运动特征来分割场景的方法。
Based on the border in the shot clustering algorithm, we proposed the method of using the camera frame color image characteristics and the overall situation of movement to the scene segmentation.
在基于镜头边界的聚类算法基础上，提出了利用镜头帧图像的全局颜色特征和运动特征来分割场景的方法。
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