GPU Hierarchical Quilted Self Organizing Maps for multimedia understanding

Youssef S G Nashed*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

It is well established that the human brain outperforms current computers, concerning pattern recognition tasks, through the collaborative processing of simple building units (neurons). In this work we expand an abstracted model of the neocortex called Hierarchical Quilted Self Organizing Map, benefiting from the parallel power of current Graphical Processing Units, to achieve realtime understanding and classification of spatio-temporal sensory information. We also propose an improvement on the original model that allows the learning rate to be automatically adapted according to the input training data available. The overall system is tested on the task of gesture recognition from a Microsoft Kinect publicly available dataset.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012
Pages491-492
Number of pages2
DOIs
StatePublished - Dec 1 2012
Event14th IEEE International Symposium on Multimedia, ISM 2012 - Irvine, CA, United States
Duration: Dec 10 2012Dec 12 2012

Other

Other14th IEEE International Symposium on Multimedia, ISM 2012
CountryUnited States
CityIrvine, CA
Period12/10/1212/12/12

Keywords

  • GPU
  • Memory prediction framework
  • Neural networks
  • Self Organizing Maps
  • Temporal classification

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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