  The human action classification task is a widely researched topic and is
still an open problem. Many state-of-the-arts approaches involve the usage of
bag-of-video-words with spatio-temporal local features to construct
characterizations for human actions. In order to improve beyond this standard
approach, we investigate the usage of co-occurrences between local features. We
propose the usage of co-occurrences information to characterize human actions.
A trade-off factor is used to define an optimal trade-off between vocabulary
size and classification rate. Next, a spatio-temporal co-occurrence technique
is applied to extract co-occurrence information between labeled local features.
Novel characterizations for human actions are then constructed. These include a
vector quantized correlogram-elements vector, a highly discriminative PCA
(Principal Components Analysis) co-occurrence vector and a Haralick texture
vector. Multi-channel kernel SVM (support vector machine) is utilized for
classification. For evaluation, the well known KTH as well as the challenging
UCF-Sports action datasets are used. We obtained state-of-the-arts
classification performance. We also demonstrated that we are able to fully
utilize co-occurrence information, and improve the standard bag-of-video-words
approach.
