信息论联合聚类算法及其在视频镜头聚类中的应用

Information-Theoretic Co-Clustering for Video Shot Categorization

视频镜头自动聚类是基于内容索引与检索领域中的重要研究课题.以往相关工作,缺乏考虑描述镜头内容的特征与特征间存在关联性以及关联特征对镜头相似性度量和镜头聚类性能带来的影响.为提供更合理的镜头相似性度量,该文基于信息论联合聚类算法,将特征关联性挖掘和镜头聚类描述为彼此依附的同步优化过程.同时,为自动估计视频中镜头类别数,文中还提出基于贝叶斯信息准则的类别数估计算法.

Automatic categorization of video shots is very useful in video content analysis applications, such as structure parsing and semantic event extraction. In previous works, various lowlevel features, including color, texture, motion, have been used to describe the video shots. Based on these features, the similarities between shots are measured for further categorization. However, in the similarity measure, most current works treat all these feature dimensions independently, and seldom consider the potential correlations between different kinds of features. In order to explore the relationships between different features and provide a more accurate similarity measure for video shot categorization, in this paper, authors formulate the problem of unsupervised shot clustering in the scheme of information-theoretic co-clustering. In this scheme, a two-way clustering is performed to group video shots and video features simultaneously. In addition, Bayesian information criterion is employed to automatically estimate the number of clusters for both the video shots and the descriptive features. Evaluations on 1374 shots extracted from around 4-hour sports video shows very encouraging results in comparison with the traditional oneway clustering algorithm.