A Recursive Constrained Framework for Unsupervised Video Action Clustering
IEEE Transactions on Industrial Informatics(2020)
摘要
Video action understanding is an active field of intelligent video analytics, and contextual information in the videos has gained lots of attention for better action understanding. However, most existing works focus on using contextual information for supervised or semi-supervised analysis, and how to effectively use contextual information to boost the unsupervised action clustering performance is still a challenging problem. In this article, we propose a recursive constrained framework for unsupervised video action clustering by utilizing the contextual information of the action and scene. Considering the unique contextual characteristics of video action, action context clustering solution and scene context clustering solution are obtained simultaneously. Based on these two solutions, a recursive priori propagation is proposed to exploit information gain of the priori clustering solutions, and then the information gain is fed back into the procedures of both subspace representation and spectral clustering. Specifically, to explore the unknown relationships in the priori clustering solutions, the constraint-guided subspace representation is introduced by fusing the recursive priori constraint into the self-representation model. Taking priori information and multiview features into consideration, the priori-inherited multiview spectral clustering is proposed to obtain more discriminative spectral embeddings for action clustering. Experiments on three video benchmark datasets demonstrate that the proposed method outperforms state-of-the-art methods.
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关键词
Clustering methods,Informatics,Clustering algorithms,Image reconstruction,Visual analytics,Indexes,Face
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