A multiclass ELM strategy in pose-based 3D human motion analysis

Arif Budiman, Mohamad Ivan Fanany

Research output: Contribution to conferencePaper

Abstract

This paper pursues the best multiclass classification strategy for pose-based 3D human motion recognition using Extreme Learning Machines (ELM). Such classification task is one of the most difficult classification problem because the pose is not unique and the same pose might be randomly distributed inside any unrelated and absolutely different activities. In this study, bag of poses are selected as features and several multiclass classification strategies commonly used in binary classifiers such as Support Vector Machines (SVM) are adopted into the ELM implementation and then compared with non-binary classifications of ELM and binary classifications of SVM. A number of multiclass strategies such as One-Against-All (OAA), One-Against-One (OAO), Directed Acyclic Graph (DAG), hierarchical binary tree, and OAO-3Tree are evaluated and analysed. We found that the OAO-3Tree strategy using Max-Win vote fusion of labeled output function gives the best result.

Original languageEnglish
Pages341-346
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013 - Bali, Indonesia
Duration: 28 Sep 201329 Sep 2013

Conference

Conference2013 5th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013
CountryIndonesia
CityBali
Period28/09/1329/09/13

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