Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering
Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering
复制标题
DOI：
10.1109/tcsvt.2011.2147190
复制
发表时间：
2011-11
影响因子：
8.4
通讯作者：
Hong Lu;Yap-Peng Tan;X. Xue
中科院分区：
文献类型：
--
作者：
Hong Lu;Yap-Peng Tan;X. Xue
在本文中，我们提出了一种有效的，自适应的，基于局部图分割方法的视频场景聚类。首先，提出了一种图划分方法来组视频镜头到场景中，和一个对等组过滤（PGF）计划是用来识别所有的镜头相似的每个特定的镜头基于Fisher的判别分析。工作与可计算的镜头相似性的措施，只有有限的鉴别力，我们开发了一个图形分区方案，通过最大限度地提高同一集群内的镜头相似性和最小化，不同的集群之间的镜头聚类。其次，考虑到视频数据通常是按顺序获取和观看的，我们提出了一种基于局部的PGF和图分割的视频片段50镜头，100镜头等，这种基于局部的方法的优点是，场景簇的数量不需要事先知道，它可以实现性能相当于整个视频序列的处理。实验结果证明了该方法的有效性和效率。
We propose in this paper an efficient, adaptive, and locality-based graph partitioning method for video scene clustering. First, a graph partitioning method is proposed to group video shots into scenes, and a peer-group filtering (PGF) scheme is used to identify all the shots similar to each particular shot based on Fisher's discriminant analysis. To work with computable shot similarity measures that have only limited discriminating power, we develop a graph partitioning scheme to cluster the shots by maximizing the likeness of shots within the same cluster and minimizing that between different clusters. Second, considering that video data are normally obtained and viewed sequentially, we propose to perform a locality-based PGF and graph partitioning on video segments with 50 shots, 100 shots, and so on. This proposed locality-based method has the advantage that the number of scene clusters is not required to be known a priori, and it can achieve performance comparable to that processing on the whole video sequence. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed method.