RI: Small: Temporal Causality For Video Event Analysis

该项目正在寻求一种新的策略，通过利用时间因果关系的统计测试来分析视频中的时间结构。其动机是需要无监督的视频分析方法，不需要一个预定义的视频类别集或标记的例子的大型语料库。我们的出发点是经典的格兰杰因果关系公式，它为两个时间序列之间的直接影响提供了一个原则性的统计检验。对经典的成对格兰杰检验进行了改进，得到了一种适用于视频事件的多点过程检验方法。使用这种表示，正在开发的方法分组的视觉单词集的基础上，随着时间的推移，他们的互动。这导致了一种新的自下而上的分割方法，可以识别视觉词之间的相互作用，而无需监督。另一个目标是开发一种综合方法来建模视觉事件和识别因果关系。额外的努力旨在开发新的功能，从因果关系构建的目标，提高分类和检索任务的性能。总之，该项目正在开发新的无监督方法，用于基于时间因果分析来表示和分割视频。由此产生的算法在视频检索和分类任务中提高了性能，并提供了组织和搜索非结构化内容（如YouTube视频）的新方法。用于视频分割和分类的新数据集正在沿着一个分析软件库一起开发，以促进研究界的采用。

This project is pursuing a novel strategy for the analysis of temporal structure in video through the exploitation of statistical tests of temporal causality. The motivation is the need for unsupervised video analysis methods which do not require a pre-defined set of video categories or a large corpus of labeled examples. The starting point is the classical formulation of Granger causality, which provides a principled statistical test for directed influence between two time series. Modifying the classical pair-wise Granger test leads to a method which is suitable for video events, which are represented as multiple point processes. Using this representation, methods are being developed for grouping visual words into sets based on their interaction over time. This results in a novel bottom-up segmentation approach which can identify interactions between visual words without supervision. A further goal is the development of an integrated approach to modeling visual events and identifying causal relations. Additional efforts are aimed at developing novel features constructed from causal relations with the goal of improved performance on categorization and retrieval tasks. In summary, the project is developing new unsupervised methods for representing and segmenting video based on temporal causal analysis. The resulting algorithms yield improved performance in video retrieval and categorization tasks, and provide new approaches to organizing and searching unstructured content such as YouTube videos. Novel datasets for video segmentation and categorization are being developed along with a library of analysis software to facilitate adoption by the research community.