Toward a causal topic model for video scene analysis
Toward a causal topic model for video scene analysis
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DOI：
10.1109/ijcnn.2013.6706941
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
John McCaffery;A. Maida
中科院分区：
文献类型：
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作者：
John McCaffery;A. Maida
视频数据中不同类型活动的无监督有许多应用，如异常检测、视频搜索的自动标记和认知建模。主题模型最初用于语料库分析，最近被用于识别视频中不同类型的活动。在主题模型中，概率潜在语义分析（pLSA）为识别视频中的活动簇提供了一种有效的方法。本文将pLSA与Pearl[1]的因果图模型相结合，同时学习视觉事件结构及其时间关系。该模型是完全生成的。噪声或风格的时间依赖用于学习，众所周知，它可以识别与人类学习者相同的因果模式。时间学习的添加允许系统对时间有序和长期时间依赖性进行建模，这是传统主题模型无法做到的。该模型成功地识别了视频中人类可识别的事件结构，并成功地对人类活动学习视频进行了分类。
Unsupervised of different types of activity in video data has many applications such as anomaly detection, automated tagging of video for search, and cognitive modeling. Topic models originally used in corpus analysis have recently been used to identify different types of activities in videos. Among topic models, probabilistic latent semantic analysis (pLSA) provides an efficient method for identifying clusters of activity in video. This paper integrates pLSA with the causal graphical models of Pearl [1] to learn visual event structures and their temporal relationships simultaneously. The model is fully generative. A noisy-OR style temporal dependence is used for learning which is well known to identify the same causal patterns that human learners do. The addition of temporal learning allows the system to model temporally ordered and long range temporal dependencies that traditional topic models cannot. The model successfully identifies human recognizable event structures in video and successfully classifies videos of human activity learning.