摘要
如何从动态视频图像大量的冗余信息中提取关键信息,进而有效地存储和检索视频成为当前本领域研究的热点。模型通过对图像帧进行分块,再对块图像帧进行预处理,基于熵密度的比较选择初始聚类中心,进而确定初始聚类半径,应用归一运算,合并同类图像帧以生成关键帧,用来表征图像的主要内容。实验表明该文模型能够减少动态视频信息中的冗余度,同时还能有效地还原视频真实内容,对于视频存储和检索具有非常重要的意义。
How to extract the key information from large amount of redundant information of dynamic video images,and then effectively store and retrieve video has become research hotspots in the field at present.The model divides image frames into blocks,and then pre-process block image frames.Based on the comparison of entropy density,the initial clustering center is selected,and then the initial clustering radius is determined.The normalization operation is applied and the same kind of image frames are combined to generate key frames,which are used to represent the main content of the image.The experiment shows that the model proposed in this paper can reduce the redundancy of dynamic video information and effectively restore the true content of video.It is very important for video storage and retrieval.
作者
向东
吉静
张景瑞
欧阳泉
XIANG Dong;JI Jing;ZHANG Jingrui;OUYANG Quan(Wuhan Xingtu Xinke Electronics Co.,Ltd,Wuhan 430073,China;School of Aerospace Engineering,Xiamen University,Xiamen 361102,China)
出处
《现代信息科技》
2021年第3期9-12,16,共5页
Modern Information Technology
基金
武汉省科技局科技项目(2019010702011291)
广东省自然科学基金项目(2019A1515010411)。
