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中文摘要：
针对关键帧提取问题,提出了一种基于压缩感知理论和熵计算的关键帧提取算法,首先通过构造符合有限等距性质要求的稀疏随机投影矩阵,将高维多尺度帧图像特征变换为低维多尺度帧图像特征,并形成视频镜头低维多尺度特征列向量组;然后通过随机权值向量与低维多尺度特征向量的阿达玛乘积运算生成各帧图像的匹配特征,并根据匹配特征的相似性度量完成镜头内部的子镜头分割;最后通过交叉熵计算在每个子镜头中得到可能的关键帧,并由图像熵计算确定最终的关键帧。实验表明,与传统方法相比,本文算法提取的关键帧能够更精确、更稳定描述视频镜头内容。
英文摘要：
Key frame extraction method is regarded as one of the most important issues in content-based video retrieve（CBVR）technology.In this paper,an efficient and stable key frame extraction algorithm based on compressive sensing and entropy computing is proposed.Firstly a very sparse random projection matrix that satisfies the condition of restricted isometry property（RIP）is constructed,which is used to convert the high dimensional multi-scale frame image feature to low dimensional multi-scale frame image feature in order to generate the column vector group of the low dimensional multi-scale feature for each video shot,and then the matching feature of each frame in one shot is calculated one by one through Hadamard product between a random weights vector and the low dimensional multi-scale feature of the frame.In the next step,the Euclidean similarity measurement between adjacent matching features is used to perform sub-shot segmentation in each shot,and finally two possible key frames are obtained in every sub-shot through cross-entropy computing and the ultimate key frame is selected by image entropy computing to represent the content of the sub-shot.Our experimental results demonstrate that key frames extracted by the proposed method can describe contents of video shots more accurately and stably than the traditional methods.
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Universal blind image quality assessment using contourlet transform and singular-value decomposition
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