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Key Frames Extraction Based on Local Features for Efficient Video Summarization

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10016))

Abstract

Key frames are the most representative images of a video. They are used in different areas in video processing, such as indexing, retrieval and summarization. In this paper we propose a novel approach for key frames extraction based on local feature description. This approach will be used to summarize the salient visual content of videos. First, we start by generating a set of candidate keyframes. Then we detect interest points for all these candidate frames. After that we will compute repeatability between them and stock the repeatability values in a matrix. Finally we will model repeatability table by an oriented graph and the selection of keframe is inspired from shortest path algorithm A*. Realized experiments on challenging videos show the efficiency of the proposed method: it demonstrates that it is able to prevent the redundancy of the extracted key frames and maintain minimum requirements in terms of memory space.

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Authors and Affiliations

  1. Research Team Systèmes Intelligents en Imagerie et Vision Artificielle SIIVA– RIADI Laboratory ISI, 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Hana Gharbi, Mohamed Massaoudi, Sahbi Bahroun & Ezzeddine Zagrouba

Authors
  1. Hana Gharbi
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  2. Mohamed Massaoudi
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  3. Sahbi Bahroun
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  4. Ezzeddine Zagrouba
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Corresponding author

Correspondence to Hana Gharbi .

Editor information

Editors and Affiliations

  1. Université Paris-Sud 11 , Orsay, France

    Jacques Blanc-Talon

  2. University of Salento , Lecce, Lecce, Italy

    Cosimo Distante

  3. Ghent University , Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre , Sydney, New South Wales, Australia

    Dan Popescu

  5. University of Antwerp , Wilrijk, Belgium

    Paul Scheunders

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Gharbi, H., Massaoudi, M., Bahroun, S., Zagrouba, E. (2016). Key Frames Extraction Based on Local Features for Efficient Video Summarization. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_25

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  • DOI: https://doi.org/10.1007/978-3-319-48680-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48679-6

  • Online ISBN: 978-3-319-48680-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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