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Combining Multi-class Maximum Margin Classification with Linear Discriminant Analysis for Human Action Recognition

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

Details

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages4180-4184
Number of pages5
ISBN (Electronic)978-1-4673-9961-6
ISBN (Print)978-1-4673-9962-3
DOIs
Publication statusPublished - 2016
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Abstract

In this paper, a new multi-class classification method is proposed and evaluated in the problem of human action recognition in unconstrained environments. The proposed method exploits both the maximum margin property of multi-class Support Vector Machines and Linear Discriminant Analysis-based discrimination. Experiments indicate that by exploiting such discriminant information in a multi-class maximum margin framework, classification performance can be enhanced, leading to state-of-the-art performance in human action recognition.

Publication forum classification

Field of science, Statistics Finland