Attribute Regularization Based Human Action Recognition
Attribute Regularization Based Human Action Recognition
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DOI：
10.1109/tifs.2013.2258152
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发表时间：
2013-10
影响因子：
6.8
通讯作者：
Zhong Zhang;Chunheng Wang;Baihua Xiao;Wen Zhou;Shuang Liu
中科院分区：
文献类型：
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作者：
Zhong Zhang;Chunheng Wang;Baihua Xiao;Wen Zhou;Shuang Liu
近年来，属性作为一种高级语义信息被引入到分类中，以提高分类的准确率。多任务学习是实现这一目标的有效方法，它共享属性和动作之间的低层特征。然而，这样的方法忽略了属性对类的约束，这可能无法约束属性和动作之间的语义关系。在本文中，我们明确考虑这种属性-动作关系的人类动作识别，相应地，我们修改了多任务学习模型，增加属性正则化。通过这种方式，学习的模型不仅共享低层特征，而且还根据语义约束进行正则化。此外，由于属性和类标签包含不同数量的语义信息，我们分别对待属性分类器和动作分类器的多任务学习的框架，进一步提高性能。我们的方法在三个具有挑战性的数据集（KTH，UIUC和奥林匹克运动）上进行了验证，实验结果表明，我们的方法在人体动作识别方面取得了比以前的方法更好的结果。
Recently, attributes have been introduced as a kind of high-level semantic information to help improve the classification accuracy. Multitask learning is an effective methodology to achieve this goal, which shares low-level features between attributes and actions. Yet such methods neglect the constraints that attributes impose on classes, which may fail to constrain the semantic relationship between the attributes and actions. In this paper, we explicitly consider such attribute-action relationship for human action recognition, and correspondingly, we modify the multitask learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. In addition, since attribute and class label contain different amounts of semantic information, we separately treat attribute classifiers and action classifiers in the framework of multitask learning for further performance improvement. Our method is verified on three challenging datasets (KTH, UIUC, and Olympic Sports), and the experimental results demonstrate that our method achieves better results than that of previous methods on human action recognition.