Spectral learning of latent semantics for action recognition

This paper proposes novel spectral methods for learning latent semantics (i.e. high-level features) from a large vocabulary of abundant mid-level features (i.e. visual keywords), which can help to bridge the semantic gap in the challenging task of action recognition. To discover the manifold structure hidden among mid-level features, we develop spectral embedding approaches based on graphs and hypergraphs, without the need to tune any parameter for graph construction which is a key step of manifold learning. In particular, the traditional graphs are constructed by linear reconstruction with sparse coding. In the new embedding space, we learn high-level latent semantics automatically from abundant mid-level features through spectral clustering. The learnt latent semantics can be readily used for action recognition with SVM by defining a histogram intersection kernel. Different from the traditional latent semantic analysis based on topic models, our two spectral methods for semantic learning can discover the manifold structure hidden among mid-level features, which results in compact but discriminative high-level features. The experimental results on two standard action datasets have shown the superior performance of our spectral methods.