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

Authors:
Feng Zheng;Ling Shao;Zhan Song
Affiliations:
Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, Hong Kong, China;University of Sheffield, UK;Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, Hong Kong, China
Venue:
Proceedings of the ACM International Conference on Image and Video Retrieval
Year:
2010

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Abstract

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.