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Dynamic action recognition based on dynemes and Extreme Learning Machine

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)1890-1898
Number of pages9
JournalPattern Recognition Letters
Volume34
Issue number15
DOIs
Publication statusPublished - 2013
Publication typeA1 Journal article-refereed

Abstract

In this paper, we propose a novel method that performs dynamic action classification by exploiting the effectiveness of the Extreme Learning Machine (ELM) algorithm for single hidden layer feedforward neural networks training. It involves data grouping and ELM based data projection in multiple levels. Given a test action instance, a neural network is trained by using labeled action instances forming the groups that reside to the test sample's neighborhood. The action instances involved in this procedure are, subsequently, mapped to a new feature space, determined by the trained network outputs. This procedure is performed multiple times, which are determined by the test action instance at hand, until only a single class is retained. Experimental results denote the effectiveness of the dynamic classification approach, compared to the static one, as well as the effectiveness of the ELM in the proposed dynamic classification setting.

Keywords

  • Activity recognition, Dynamic classification, Extreme Learning Machine, Fuzzy vector quantization