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A heuristic algorithm for video scene detection using shot cluster sequence analysis

Authors:

B. ChandaAuthors Info & Claims

ICVGIP '10: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing

Pages 464 - 471

https://doi.org/10.1145/1924559.1924621

Published: 12 December 2010 Publication History

Abstract

In this paper, we present a novel scheme for segmenting video data into scenes. Based on visual similarity, the shots are first classified into clusters using modified k-means algorithm. Number of optimal clusters is decided using cluster validity analysis based on Davies-Bouldin index. Each shot is assigned a tag denoting the cluster it belongs to. Thus, the video data is represented by a sequence of cluster tags. The sequence is then analyzed by introducing the concept of stable and quasi-stable state. The elements of the sequence are merged into states and isolated elements are linked with the states to generate the scenes. The scheme is free from the dependency on critical parameters and capable of handling different types of scenes.

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Cited By

Kuanar SRanga KChowdhury A(2015)Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest ClusteringIEEE Transactions on Multimedia10.1109/TMM.2015.244355817:8(1166-1173)Online publication date: Aug-2015
https://doi.org/10.1109/TMM.2015.2443558
Parmar MAngelides M(2015)MAC-REALM: A Video Content Feature Extraction and Modelling FrameworkThe Computer Journal10.1093/comjnl/bxv04258:9(2135-2171)Online publication date: 29-Jun-2015
https://doi.org/10.1093/comjnl/bxv042
Baber JSatoh SAfzulpurkar NKeatmanee CChua TLu KMei TWu X(2013)Bag of visual words model for videos segmentation into scenesProceedings of the Fifth International Conference on Internet Multimedia Computing and Service10.1145/2499788.2499814(191-194)Online publication date: 17-Aug-2013
https://dl.acm.org/doi/10.1145/2499788.2499814
Show More Cited By

Index Terms

A heuristic algorithm for video scene detection using shot cluster sequence analysis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
        Video summarization
  2. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Other conferences

ICVGIP '10: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing

December 2010

533 pages

ISBN:9781450300605

DOI:10.1145/1924559

General Chairs:
Rama Chellappa
University of Maryland
,
Padmanabhan Anandan
Microsoft Research, India
,
Program Chairs:
A. N. Rajagopalan
Indian Institute of Technology Madras, India
,
P. J. Narayanan
International Institute of Information Technology Hyderabad, India
,
Philip Torr
Oxford Brookes University, UK

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2010

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ICVGIP '10

ICVGIP '10: Seventh Indian Conference on Computer Vision, Graphics and Image Processing

December 12 - 15, 2010

Chennai, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Kuanar SRanga KChowdhury A(2015)Multi-View Video Summarization Using Bipartite Matching Constrained Optimum-Path Forest ClusteringIEEE Transactions on Multimedia10.1109/TMM.2015.244355817:8(1166-1173)Online publication date: Aug-2015
https://doi.org/10.1109/TMM.2015.2443558
Parmar MAngelides M(2015)MAC-REALM: A Video Content Feature Extraction and Modelling FrameworkThe Computer Journal10.1093/comjnl/bxv04258:9(2135-2171)Online publication date: 29-Jun-2015
https://doi.org/10.1093/comjnl/bxv042
Baber JSatoh SAfzulpurkar NKeatmanee CChua TLu KMei TWu X(2013)Bag of visual words model for videos segmentation into scenesProceedings of the Fifth International Conference on Internet Multimedia Computing and Service10.1145/2499788.2499814(191-194)Online publication date: 17-Aug-2013
https://dl.acm.org/doi/10.1145/2499788.2499814
Mohanta PChowdhury SRoy ASaha SChanda B(2013)Static Summarization of Video Scenes Based on Minimal Spanning TreePattern Recognition and Machine Intelligence10.1007/978-3-642-45062-4_60(437-444)Online publication date: 2013
https://doi.org/10.1007/978-3-642-45062-4_60

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