Multi-keyframe abstraction from videos

Ping Li, Yanwen Guo, Hanqiu Sun

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

This paper presents a method for abstracting multi-keyframe from video datasets. Existing video abstraction methods focused on simple view videos, and the results will be unacceptable if applied to overlapping views directly due to limitations like unavoidable redundancy and complicated inner correlations. We propose a correlation map to naturally model the correlations with various attributes among multi-keyframe, keyframe importance and weighted correlations are then computed to construct the map. The weighted correlations, unlike the unweighted ones, not only model probabilistic relationship among keyframes but also address the temporal and visual similarity. We facilitate the abstraction process via SVM classification and keyframes reduction using rough set. The multi-keyframe correlation map, which serially assembles event-centered keyframes in temporal order, is presented for displaying the abstraction, which shows the correlations and improves the browsability of video datasets.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages2473-2476
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11/09/1114/09/11

Keywords

  • condensed representation
  • Correlation map
  • keyframe abstraction
  • video summarization

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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