Earticle

초록

영어

With the emergence of numerous 3D human motion capture databases, the effective analysis and handling of human motion data have become a major challenge so that the use of motion capture databases can be maximized. To reduce the high-dimensional complexity of data, a type of geometrical feature based on 2D geometrical space law is first extracted from human motion for the application of motion data into a low-dimensional subspace. With the aim of achieving a low-dimensional feature, identification and classification in different motions are then conducted through spectral clustering based on manifold learning to realize the automatic identification and retrieval of 3D human motion.

목차

Abstract
 1. Introduction
 2. The Module of Feature Selection and Extraction
 3. Spectral Clustering
  3.1. Manifold Learning Spectral Clustering (MLSC)
  3.2. The Main Steps of ISOMAP Algorithm
 4. The Experimental Result and Analysis
 Acknowledgements
 References

키워드

저자

• Hongli Zhu [ School of Information and Electronic Engineering, Zhejiang University City College, Hangzhou, China ]
• Jian Xiang [ School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605