Discriminative Co-Occurrence of Concept Features for Action Recognition

Abstract

We present a new method for action recognition that employs the co-occurrence of concept features as semantic geometric context. Firstly, the semantic concept codebook is learnt by an improved subspace clustering, then the spatio-temporal interest points are labelled as meaningful features, namely concept features. After that, Multi-scale co-occurrence statistics that embeds the relative distance and direction of pairwise concept features is constructed. Unlike the traditional k-means, the features labelled by the concept codebook well represent the ingredients of objects and ensure temporal consistency. Moreover, the relative layout is the semantic geometric context that describes the changes of geometric relationships. Using the popular KTH and UCF-sports action datasets, experimental results show that the relative layouts combined with the STIPs have discriminative power for action recognition. Our method obtains promising recognition performance compared with other state-of-the-art algorithms.