| 潘磊,束鑫,程科,张明.基于压缩感知和熵计算的关键帧提取算法[J].光电子激光,2014,(10):1977~1982 |
| 基于压缩感知和熵计算的关键帧提取算法 |
| A key frame extraction algorithm based on compressive sensing and entropy computing |
| 投稿时间：2014-06-03 |
| DOI： |
| 中文关键词: 关键帧 压缩感知 子镜头 熵计算 |
| 英文关键词:key frame compressive sensing sub-shot entropy computing |
| 基金项目:国家自然科学基金(51008143,61103128,61170120)、江苏省自然科 学基金(BK20130473)；江苏省科技创新与成果转化(重大科技成果转化)(BA2012129)、江苏省研究生科研创新计划(1252209AK)和江苏大学现代农业装备与技术省部共建教育部重点实验室开放基金(NZ201303) 资助项目 (1.江苏科技大学 计算机科学与工程学院,江苏 镇江 212003； 2.江苏大学 现代农业装备与技术省部共建教育部重点实验室,江苏 镇江 212013) |
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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 i ssues in content-based video retrieve (CBVR) technology.In this paper,an efficient and stable key frame ext raction algorithm based on compressive sensing and entropy computing is proposed.Firstly a very sparse ran dom projection matrix that satisfies the condition of restricted isometry property (RIP) is constructed,whi ch is used to convert the high dimensional multi-scale frame image feature to low dimensional multi-scale fra me 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 Hada mard 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 segm entation in each shot,and finally two possible key frames are obtained in every sub-shot through cross-entropy c omputing 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 conten ts of video shots more accurately and stably than the traditional methods. |
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