Novel kernel-based recognizers of human actions

Somayeh Danafar, Alessandro Giusti, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


We study unsupervised and supervised recognition of human actions in video sequences. The videos are represented by probability distributions and then meaningfully compared in a probabilistic framework. We introduce two novel approaches outperforming state-of-the-art algorithms when tested on the KTH and Weizmann public datasets: an unsupervised nonparametric kernel-based method exploiting the Maximum Mean Discrepancy test statistic; and a supervised method based on Support Vector Machine with a characteristic kernel specifically tailored to histogram-based information. Copyright © 2010 Somayeh Danafar et al.
Original languageEnglish (US)
JournalEurasip Journal on Advances in Signal Processing
StatePublished - Jul 20 2010
Externally publishedYes

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering


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