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中文摘要：
关键帧提取是基于内容的视频检索技术的关键问题.文中提出一种基于多重压缩感知和距离计算的关键帧提取算法,首先将镜头内的各帧图像分割为若干不相交的块,通过滤波器生成块的高维特征;然后利用多个不同的、具有有限等距性质的稀疏矩阵对块高维特征进行采样,将采样的均值作为块的低维特征;采用多种距离计算相邻帧对应块之间的差异,完成子镜头的分割操作,在每个子镜头内部,选取与该子镜头平均内容最接近的帧作为关键帧.实验表明,该算法提取出的关键帧能够准确描述镜头的主要内容.
英文摘要：
Key frame extraction is considered as the key issue of content-based video retrieval. An algorithm based on multiple compressive sensing and distances＇ computing is proposed. In the first step,each frame in one shot is segmented into several disjoint blocks,high dimensional features of which are generated by filtering.Then,multiple different sparse matrices that satisfy restricted isometry property are employed to sample the high dimensional feature of each block,and the mean value of sampling is calculated as the low dimensional feature of each block. Several different distances are used to compute differences between corresponding blocks of neighboring frames to conduct sub-shot segmentation. The frame nearest to the average content of each sub-shot is selected as the key frame. Experimental results demonstrate that key frames extracted by the proposed algorithm can describe the main content of shot accurately.
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同项目期刊论文
Universal blind image quality assessment using contourlet transform and singular-value decomposition
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