Supervised neighborhood topology learning for human action recognition

Jinhua Ma*, Pong Chi YUEN, Weiwen Zou, Jian Huang Lai

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Supervised manifold learning has been successfully applied to human action recognition. With the class label information, the recognition performance can be improved. However, the learned manifold may not be able to well preserve the local structure which reflects temporal information of an action. To overcome this limitation, this paper proposes a new supervised manifold learning algorithm namely supervised neighborhood topology learning (SNTL) for human action recognition. SNTL is based on the framework of locality preserving projection (LPP). Different from LPP, SNTL constructs the adjacency graph with a topology defined in a supervised manner, which not only separates data points from different actions but also preserves the local structure of data points from the same action. With the advantage of locality preserving property in the framework of LPP, SNTL provides good discriminant ability and preserves temporal information of each action contained in local structure. Weizmann human action database is used for evaluation. Experimental results show that the method achieves 95.56% recognition accuracy.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Pages476-481
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan
Duration: 27 Sep 20094 Oct 2009

Publication series

Name2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Conference

Conference2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Country/TerritoryJapan
CityKyoto
Period27/09/094/10/09

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Supervised neighborhood topology learning for human action recognition'. Together they form a unique fingerprint.

Cite this