Eigen-space learning using semi-supervised diffusion maps for human action recognition

Human actions can be seen as a trajectory in the eigen-space of silhouette of the human body. In this paper, the silhouette is firstly denoted as a vector using R-transform. Then, we exploit semi-supervised diffusion maps (SSDM) for dimensionality reduction and learning the eigen-space of the silhouette. Semi-supervised diffusion maps characterizes the spatiotemporal property of the action, as well as to preserve much of the local geometric structure and label information. We use the K-nearest neighbor classifier for recognizing actions represented as histograms of occurrence of the silhouette in the eigen-space. Experimental results show that the proposed approach performs significantly better than other manifold learning based action recognition techniques.