Tampere University of Technology

TUTCRIS Research Portal

Active classification for human action recognition

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3249-3253
Number of pages5
DOIs
Publication statusPublished - 2013
Publication typeA4 Article in a conference publication
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Abstract

In this paper, we propose a novel classification method involving two processing steps. Given a test sample, the training data residing to its neighborhood are determined. Classification is performed by a Single-hidden Layer Feedforward Neural network exploiting labeling information of the training data appearing in the test sample neighborhood and using the rest training data as unlabeled. By following this approach, the proposed classification method focuses the classification problem on the training data that are more similar to the test sample under consideration and exploits information concerning to the training set structure. Compared to both static classification exploiting all the available training data and dynamic classification involving data selection for classification, the proposed active classification method provides enhanced classification performance in two publicly available action recognition databases.

Keywords

  • Active classification, dynamic classification, Extreme Learning Machine, human action recognition, Single-hidden Layer Feedforward Neural network