  This paper proposes a novel Subdivision-Fusion Model (SFM) to recognize human
actions. In most action recognition tasks, overlapping feature distribution is
a common problem leading to overfitting. In the subdivision stage of the
proposed SFM, samples in each category are clustered. Then, such samples are
grouped into multiple more concentrated subcategories. Boundaries for the
subcategories are easier to find and as consequence overfitting is avoided. In
the subsequent fusion stage, the multi-subcategories classification results are
converted back to the original category recognition problem. Two methods to
determine the number of clusters are provided. The proposed model has been
thoroughly tested with four popular datasets. In the Hollywood2 dataset, an
accuracy of 79.4% is achieved, outperforming the state-of-the-art accuracy of
64.3%. The performance on the YouTube Action dataset has been improved from
75.8% to 82.5%, while considerably improvements are also observed on the KTH
and UCF50 datasets.
