Motion dense sampling for video classification

Kazuaki Aihara, Terumasa Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose the motion dense sampling (MDS) for video classification, which detects very informative interest points from video frames. MDS has two advantages compared to other existing methods. The first advantage is that MDS detects only interest points which belong to foreground regions of all regions of a video frame. Also it can detect the constant number of points even when the size of foreground region in an image drastically changes. The Second one is that MDS enable to describe scale invariable features by computing sampling scale for each frame based on the size of foreground regions. Thus, our method detects much more informative interest points from videos than other methods. Experimental results show a significant improvement over existing methods on YouTube dataset. Our method achieves 86.8% accuracy for video classification by using only one descriptor.

Original languageEnglish
Title of host publication2013 International Conference on IT Convergence and Security, ICITCS 2013
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 3rd International Conference on IT Convergence and Security, ICITCS 2013 - Macau, China
Duration: 2013 Dec 162013 Dec 18

Other

Other2013 3rd International Conference on IT Convergence and Security, ICITCS 2013
Country/TerritoryChina
CityMacau
Period13/12/1613/12/18

ASJC Scopus subject areas

  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Motion dense sampling for video classification'. Together they form a unique fingerprint.

Cite this