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

Authors:

Zhan SongAuthors Info & Claims

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

Pages 151 - 157

https://doi.org/10.1145/1816041.1816066

Published: 05 July 2010 Publication History

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.

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Cited By

Glorindal Selvam GGnanadurai D(2014)Efficient Silhouette-based Input Methods for Reliable Human Action Recognition from VideosArtificial Intelligence and Evolutionary Algorithms in Engineering Systems10.1007/978-81-322-2135-7_54(503-511)Online publication date: 26-Nov-2014
https://doi.org/10.1007/978-81-322-2135-7_54
Zheng FShao LSong ZLi SGao XSebe N(2010)A set of co-occurrence matrices on the intrinsic manifold of human silhouettes for action recognitionProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1816041.1816108(454-461)Online publication date: 5-Jul-2010
https://dl.acm.org/doi/10.1145/1816041.1816108

Index Terms

Eigen-space learning using semi-supervised diffusion maps for human action recognition
1. Computing methodologies
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Information

Published In

cover image ACM Conferences

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2010

492 pages

ISBN:9781450301176

DOI:10.1145/1816041

Conference Chairs:
Shipeng Li
Microsoft Research Asia, China
,
Xinbo Gao
Xidian University, China
,
Nicu Sebe
University of Trento, Italy

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2010

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Shenzhen Science Plan
Chinese Academy of Sciences
National Natural Science Foundation of China

Conference

CIVR' 10

Sponsor:

SIGMM

CIVR' 10: International Conference on Image and Video Retrieval

July 5 - 7, 2010

Xi'an, China

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Cited By

Glorindal Selvam GGnanadurai D(2014)Efficient Silhouette-based Input Methods for Reliable Human Action Recognition from VideosArtificial Intelligence and Evolutionary Algorithms in Engineering Systems10.1007/978-81-322-2135-7_54(503-511)Online publication date: 26-Nov-2014
https://doi.org/10.1007/978-81-322-2135-7_54
Zheng FShao LSong ZLi SGao XSebe N(2010)A set of co-occurrence matrices on the intrinsic manifold of human silhouettes for action recognitionProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1816041.1816108(454-461)Online publication date: 5-Jul-2010
https://dl.acm.org/doi/10.1145/1816041.1816108

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