摘要
为了提高人体动作视频检索的鲁棒性和效率,提出了一种模糊加权的人体动作视频检索方法。该方法采用3D Harris算子检测视频中的时空兴趣点,提取这些兴趣点的梯度信息,构建特征向量;然后采用模糊聚类方法构建聚类特征向量,提高特征向量的抗干扰能力;匹配聚类特征向量中的梯度向量对,构建模糊权重矩阵,计算查询视频与数据库中各个视频的相似度;最后在KTH数据库上进行视频检索实验,结合精确度、召回率和检索耗时三个指标进行评价,证明该方法的有效性。
In order to improve the robustness and efficiency of video retrieval for human activity,this paper proposed a fuzzy weighted method for human activity video retrieval.This method used 3D Harris operator to detect the spatio-temporal interest points in the video,and extracted the gradient information of these points to construct feature vector to describe video.Then it used fuzzy clustering method to construct fuzzy clustering feature vector,to improve the ability of anti-interference of feature vector.And then,it matched pair of gradient vector in the fuzzy clustering feature vectors to construct fuzzy weight matrix,and calculated the similarity between the query video and each video in the database.Finally,it carried out the video retrieval experiment on the KTH database,and carried the evaluation out with three metrics of accuracy,recall and retrieval time,which proves that the performance of this method is the best.
作者
张涵
韩毅
李跃新
Zhang Han;Han Yi;Li Yuexin(College of Computer Science&Information Engineering,Anyang Institute of Technology,Anyang Henan 455000,China;National NC System Engineering Research Center,Huazhong University of Science&Technology,Wuhan 430000,China;School of Computer Science&Engineering,Hubei University,Wuhan 430000,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第3期957-960,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(64110712)
河南省科技攻关计划资助项目(1721021103)
关键词
视频检索
行为识别
模糊聚类
时空兴趣点
3D
HARRIS
video retrieval
behavior recognition
fuzzy clustering
spatio-temporal interest point
3D Harris
