论文标题
通过在线面部聚类进行电影分析的动态角色图
Dynamic Character Graph via Online Face Clustering for Movie Analysis
论文作者
论文摘要
自动化电影内容分析的有效方法涉及建立角色的网络（图）。现有工作通常会构建静态字符图，以使用元数据，脚本或手动注释来汇总内容。我们提出了一种无监督的方法来构建动态角色图，该图形捕获了角色相互作用的时间演变。我们将其称为字符相互作用图（CIG）。我们的方法具有两个组件：（i）一种在线面部聚类算法，该算法发现视频流中的字符出现时，以及（ii）使用所得簇的时间动力学同时创建CIG。我们演示了CIG对两个电影分析任务的有用性：叙事结构（ACT）细分和主要角色检索。我们对包含5000多个面部曲目的全长电影的评估表明，所提出的方法为这两个任务都取得了出色的表现。
An effective approach to automated movie content analysis involves building a network (graph) of its characters. Existing work usually builds a static character graph to summarize the content using metadata, scripts or manual annotations. We propose an unsupervised approach to building a dynamic character graph that captures the temporal evolution of character interaction. We refer to this as the character interaction graph(CIG). Our approach has two components:(i) an online face clustering algorithm that discovers the characters in the video stream as they appear, and (ii) simultaneous creation of a CIG using the temporal dynamics of the resulting clusters. We demonstrate the usefulness of the CIG for two movie analysis tasks: narrative structure (acts) segmentation, and major character retrieval. Our evaluation on full-length movies containing more than 5000 face tracks shows that the proposed approach achieves superior performance for both the tasks.
