3D motion retrieval with motion index tree

  • Authors:
  • Feng Liu;Yueting Zhuang;Fei Wu;Yunhe Pan

  • Affiliations:
  • Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, PR China;Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, PR China;Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, PR China;Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, PR China

  • Venue:
  • Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

With the development of Motion capture techniques, more and more 3D motion libraries become available. In this paper, we present a novel content-based 3D motion retrieval algorithm. We partition the motion library and construct a motion index tree based on a hierarchical motion description. The motion index tree serves as a classifier to determine the sub-library that contains the promising similar motions to the query sample. The Nearest Neighbor rule-based dynamic clustering algorithm is adopted to partition the library and construct the motion index tree. The similarity between the sample and the motion in the sub-library is calculated through elastic match. To improve the efficiency of the similarity calculation, an adaptive clustering-based key-frame extraction algorithm is adopted. The experiment demonstrates the effectiveness of this algorithm.