Human Action Attribute Learning Using Low-Rank Representations
نویسندگان
چکیده
This paper studies the problem of learning human action attributes based on union-of-subspaces model. It puts forth an extension of the low-rank representation (LRR) model, termed the hierarchical clustering-aware structure-constrained low-rank representation (HCSLRR) model, for unsupervised learning of human action attributes from video data. The effectiveness of the proposed model is demonstrated through experiments on five human action datasets for action recognition.
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تاریخ انتشار 2017