基于支配集的视频关键帧提取方法

关键帧提取是视频处理的重要步骤之一，在视频内容分析中有广泛的应用.针对基于内容的视频分析，为获取高效的视频摘要提出一种视频关键帧提取方法.该方法首先以视频帧为顶点，以顶点之间的连线构造边，利用不同帧的加速鲁棒特征点的豪斯多夫(Hausdorff)距离函数计算边权重，把视频建模成一个无向权重图，然后根据图的支配集理论把视频关键帧提取等价为无向权重图的极小支配集选取问题，进而利用整数线性规划选取图支配集，得到视频关键帧.与传统算法相比，该方法提取的关键帧依赖于视频内容，不受时间和视频镜头约束.实验结果显示，该方法能够体现关键帧的代表性和区分性，具有较高的保真度和压缩率.

Keyframe Extraction Method Based on Dominating Set

Keyframe extraction is one of the important steps in video processing, and it is popularly used in video content analysis. A keyframe extraction method is proposed in this paper for the content-based video summarization. In order to well depict the structure and relation among the frames of a video, we firstly model the video as an undirected weighted graph, where the frames of the video are taken as vertices, and the lines among vertices are taken as edges. The weights of edges are computed using the Hausdorff distances between pairs of speed-up features frame-by-frame which are local and robust features of frames. Subsequently, based on the representation of the keyframe, the process of keyframe extraction is equivalent to the selection of minimum dominating set in a graph, and integral linear programming is used to select the minimum dominating set in the graph. Finally, the keyframes are extracted according to the vertices in the obtained dominating set. We execute the proposed method on different types of videos, and evaluate the performance of the fidelity and compression ratios. Compared with the traditional methods, the proposed method is depended on video content rather than time and video shots. The experimental results show that the keyframes extracted by the proposed method have good representation and discrimination, and they also have high fidelity and compression ratios.