In this study, a graph regularized algorithm for early expression detection (EED), called GraphEED, is proposed. EED is aimed at detecting the specified expression in the early stage of a video. Existing EED detectors fail to explicitly exploit the local geometrical structure of the data distribution, which may affect the prediction performance significantly. According to manifold learning, the data in real-world applications are likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. The proposed graph Laplacian consists of two parts: 1) a $k$ -nearest neighbor graph is first constructed to encode the geometrical information under the manifold assumption and 2) the entire expressions are regarded as the must-link constraints since they all contain the complete duration information and it is shown that this can also be formulated as a graph regularization. GraphEED is to have a detection function representing these graph structures. Even with the inclusion of the graph Laplacian, the proposed GraphEED has the same computational complexity as that of the max-margin EED, which is a well-known learning-based EED, but the detection performance has been largely improved. To further make the model appropriate in large-scale applications, with the technique of online learning, the proposed GraphEED is extended to the so-called online GraphEED (OGraphEED). In OGraphEED, the buffering technique is employed to make the optimization practical by reducing the computation and storage cost. Extensive experiments on three video-based datasets have demonstrated the superiority of the proposed methods in terms of both effectiveness and efficiency.

中文翻译：

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图形正则化结构化输出支持向量机，用于在线扩展的早期表达式检测

在这项研究中，提出了一种用于早期表达检测 (EED) 的图正则化算法，称为 GraphEED。EED旨在检测视频早期的特定表情。现有的 EED 检测器无法明确利用数据分布的局部几何结构，这可能会显着影响预测性能。根据流形学习，现实世界应用中的数据很可能驻留在嵌入高维环境空间的低维子流形上。拟议的拉普拉斯图由两部分组成：1）a $k$ -首先构建最近邻图以在流形假设下对几何信息进行编码，并且 2) 整个表达式被视为必须链接约束，因为它们都包含完整的持续时间信息，并且表明这也可以表示为图正则化。GraphEED 就是要有一个代表这些图结构的检测功能。即使包含图拉普拉斯算子，所提出的 GraphEED 也具有与 max-margin EED 相同的计算复杂度，后者是众所周知的基于学习的 EED，但检测性能已大大提高。为了进一步使模型适用于大规模应用，利用在线学习技术，将所提出的 GraphEED 扩展为所谓的在线 GraphEED (OGraphEED)。在 OGraphEED 中，采用缓冲技术通过降低计算和存储成本使优化变得可行。在三个基于视频的数据集上进行的大量实验证明了所提出的方法在有效性和效率方面的优越性。