基于相对编辑相似度的近似重复视频检索和定位

Near-Duplicate Video Retrieval and Localization Using Relative Levenshtein Distance Similarity

为有效对近似重复视频进行检索和定位,提出了一种基于相对编辑相似度的检索和定位算法.算法包括基于局部特征的视频编码和基于相对编辑相似度的在线检索和定位两部分.基于局部特征的视频编码首先提取数据库视频的关键帧,然后在关键帧中提取Root-SIFT特征描述符并应用层次K-Means聚类算法构建词典,之后将关键帧量化至词袋模型的单词并编码.基于相对编辑相似度的在线检索和定位首先对查询视频进行编码,然后应用相对编辑相似度算法,筛选近似重复视频并对近似重复片段进行定位.实验结果表明,LD算法比Yeh等提出的算法在平均F1评价准则上效果要高8.55%,并且NDCR降低为原来的29%,效果提升明显.

To effectively retrieve and locate near-duplicate videos,a novel approach of video retrieval and localization was proposed based on relative Levenshtein Distance similarity（LD）.In the algorithm,two major components were included,named local descriptor based video coding and relative Levenshtein Distance similarity-based video retrieval and localization.About the local descriptor based video coding,the video key-frames were extracted firstly from data base;then Root-SIFT feature descriptors were extracted from key-frames and all descriptors were clustered to generate a codebook with the Hierarchical K-Means;lastly,each key-frame was assigned a unique visual word and code.About the relative Levenshtein Distance similarity-based video retrieval and localization,each query video was encoded firstly,and then the near-duplicate videos were filtrated,near-duplicate segments were located,and the retrieved videos were re-ranked with the relative Levenshtein Distance similarity-based algorithm.The experimental results show that the LD algorithm can achieve a 8.55% higher effect on the average F1 evaluation criterion than the algorithm proposed by Yeh et.al,and the NDCR is reduced to 29%.