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Mining Intelligence and Knowledge Exploration pp 180–191Cite as

Histogram Based Split and Merge Framework for Shot Boundary Detection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8284))

Abstract

In this paper, we propose a non-parametric approach for shot boundary detection in videos. The proposed method exploits the split and merge framework by the use of color histograms. Initially, every frame of the input video sequence undergoes color quantization and subsequently, the color histograms are computed for every quantized frame. The split and merge is driven by the fishers linear discriminant criterion function which results with a set of subsequences after several iterations which are assumed to be the shots present in the given video. The proposed method is experimentally tested on video samples from TrecVid 2002 dataset and YouTube online database. We have obtained overall accuracy of 85.5% Precision, 87.1% Recall and 86.1% F-measure for the dataset used. A comparative study of the proposed approach with the contemporary research works is also carried out.

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Authors and Affiliations

  1. Department of Studies in Computer Science, Manasagangothri, Mysore, India

    D. S. Guru & Mahamad Suhil

Authors
  1. D. S. Guru
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  2. Mahamad Suhil
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Editors and Affiliations

  1. Department of Business Information Systems, FSIC, National University of Ireland, University College Cork, O’Rahilly Buildings, Cork, Ireland

    Rajendra Prasath

  2. Research Centre in Computer Science, V.H.N.Senthikumara Nadar College (Autonomous), 626 001, Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Guru, D.S., Suhil, M. (2013). Histogram Based Split and Merge Framework for Shot Boundary Detection. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-03844-5_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03843-8

  • Online ISBN: 978-3-319-03844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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