Human action recognition based on the Grassmann multi-graph embedding

In this paper, a human action recognition method based on the kernelized Grassmann manifold learning is introduced. The goal is to find a map which transfers the high-dimensional data to a discriminative low-dimensional space by considering the geometry of the manifold. To this end, a multi-graph embedding method using three graphs named as center-class, within-class and between-class similarity graphs is proposed. These graphs capture the local and semi-global information of data which is the benefit of the proposed method. Graphs play an important role in subspace learning methods. Most of the graph-based methods ignore the geometry of the manifold-valued data because of using Euclidean distance in graph construction. So, these methods are sensitive to noise and outliers. To handle these problems, the geodesic distance is used to build the neighborhood graphs. We analyze the performance of the proposed method on both noisy (complex) and less noisy (simple) datasets. So, two geodesic distance metrics are used to calculate the geodesic distance of these datasets. Experimental results show the performance of the proposed method.