视频剪辑查询结合时空金字塔匹配的视频检索方法  

Video retrieval method based on fusion of spatio-temporal pyramid matching and video clips querying

作　　者：王崇科[1] 卫娟[1] 王少东[2] 

机构地区：[1]河南机电高等专科学校计算机科学与技术系,河南新乡453002 [2]哈尔滨工业大学计算机科学与技术学院,黑龙江哈尔滨150001

出　　处：《重庆邮电大学学报（自然科学版）》2015年第3期411-417,共7页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

基　　金：国家自然科学基金(61100097);河南省教育厅科学技术研究重点资助项目(14A510025)~~

摘　　要：针对视频检索系统中目标持续移动从而影响检索精度的问题,提出一种基于视频剪辑查询融合时空金字塔匹配(spatio-temporal pyramid matching,STPM)方法。借助基于特征分析和分类的片段编辑检测器将新的视频分割成多个片段,以元数据信息将视频片段存入数据库,利用基于逐帧特征结合弱分类器的boosting算法检测视频片段边界,针对新的查询视频进行分析和线上视频匹配,并利用时空金字塔匹配计算相关反馈值。在中佛罗里达大学(university of central Florida,UCF)数据集和You Tube运动视频上的实验验证了方法的有效性,实验结果表明,方法的平均精度可高达97.6%,相比其他几种较为新颖的匹配方法,取得了更好的检索性能。As the problem of retrieval precision is impacted by continuous movement of objects in the video retrieval sys- tem, a spatio -temporal pyramid matching （STPM） method based on video clip querying is proposed. Firstly, the clips ed- iting detector based on characteristics analysis and classification is used to divide new video into multiple clips, which are saved into database by metadata information. Then, boosting algorithm based on the characteristics of frames combined with the weak classifier is used to detect video clips border. Finally, analysis and online video matching are done for new query videos, and STPM is used to calculate relevance feedback value. The effectiveness of proposed method has been verified by experiments on UCF and YouTube active videos. Experimental results show that the average accuracy of proposed method can achieve at 97.6%. The proposed method has better retrieval performance than several other advanced matching approaches.

关 键 词：视频检索 视频剪辑查询 时空金字塔匹配(STPM) BOOSTING算法 弱分类器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]