  Representation of human actions as a sequence of human body movements or
action attributes enables the development of models for human activity
recognition and summarization. We present an extension of the low-rank
representation (LRR) model, termed the clustering-aware structure-constrained
low-rank representation (CS-LRR) model, for unsupervised learning of human
action attributes from video data. Our model is based on the union-of-subspaces
(UoS) framework, and integrates spectral clustering into the LRR optimization
problem for better subspace clustering results. We lay out an efficient linear
alternating direction method to solve the CS-LRR optimization problem. We also
introduce a hierarchical subspace clustering approach, termed hierarchical
CS-LRR, to learn the attributes without the need for a priori specification of
their number. By visualizing and labeling these action attributes, the
hierarchical model can be used to semantically summarize long video sequences
of human actions at multiple resolutions. A human action or activity can also
be uniquely represented as a sequence of transitions from one action attribute
to another, which can then be used for human action recognition. We demonstrate
the effectiveness of the proposed model for semantic summarization and action
recognition through comprehensive experiments on five real-world human action
datasets.
