Human Activities as Stochastic Kronecker Graphs
Human Activities as Stochastic Kronecker Graphs
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DOI：
10.1007/978-3-642-33709-3_10
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发表时间：
2012-10
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影响因子：
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通讯作者：
S. Todorovic
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文献类型：
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作者：
S. Todorovic
人类活动可以被看作是活动原语的时空重复。原语的两个实例及其重复都是随机的。它们可以通过生成模型图来建模，其中节点对应于基元，并且图的邻接矩阵对它们的亲和力进行编码，以用于概率分组到可观察的视频特征中。当活动的视频由捕获视频特征的时空布局的图表示时，这样的视频图可以被视为从活动的模型图概率地采样。该采样被公式化为模型的亲和矩阵的连续克罗内克乘法。所得到的克罗内克功率矩阵被视为视频图的邻接矩阵的噪声置换。本文介绍了我们的：1）模型图; 2）活动基元及其亲和性的存储器和时间有效的、弱监督的学习;以及3）旨在找到基元与观察到的视频特征之间的最佳预期对应的推理。我们的研究结果表明，UCF 50具有良好的可扩展性，并且在UCF YouTube，Olympic和Collective数据集的个人，结构化和集体活动方面具有上级性能。
A human activity can be viewed as a space-time repetition of activity primitives. Both instances of the primitives, and their repetition are stochastic. They can be modeled by a generative model-graph, where nodes correspond to the primitives, and the graph’s adjacency matrix encodes their affinities for probabilistic grouping into observable video features. When a video of the activity is represented by a graph capturing the space-time layout of video features, such a video graph can be viewed as probabilistically sampled from the activity’s model-graph. This sampling is formulated as a successive Kronecker multiplication of the model’s affinity matrix. The resulting Kronecker-power matrix is taken as a noisy permutation of the adjacency matrix of the video graph. The paper presents our: 1) model-graph; 2) memory- and time-efficient, weakly supervised learning of activity primitives and their affinities; and 3) inference aimed at finding the best expected correspondences between the primitives and observed video features. Our results demonstrate good scalability on UCF50, and superior performance to that of the state of the art on individual, structured, and collective activities of UCF YouTube, Olympic, and Collective datasets.