Stereoscopic video shot clustering into semantic concepts based on visual and disparity information

Konstantinos Papachristou, Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
331 Downloads (Pure)

Abstract

In this paper, we propose a framework for clustering shots from stereoscopic videos into clusters that correspond to semantic concepts exploiting visual and disparity information. Various color, disparity and texture descriptors are applied to shot key frames for obtaining low-level representations. Self Organizing Maps are subsequently employed upon various combinations of these representations in order to determine a lattice of representative semantic concepts. Experimental results on performances and football stereoscopic videos show that the use of disparity information leads to better clustering compared to using visual information only.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP 2014)
Subtitle of host publicationProceedings of a meeting held 27-30 October 2014, Paris, France
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5472-5476
Number of pages5
ISBN (Electronic)9781479957514
ISBN (Print)9781479957521
DOIs
Publication statusPublished - Mar 2015
EventIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014) - Reims, France
Duration: 21 Sep 201424 Sep 2014

Publication series

NameProceedings of IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014)
CountryFrance
CityReims
Period21/09/1424/09/14

Keywords

  • Semantic concepts
  • stereoscopic video
  • disparity
  • shot clustering
  • Self Organizing Map

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