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User Assisted Clustering Based Key Frame Extraction

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Advances in Computing and Data Sciences (ICACDS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1244))

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Abstract

Our study proposes a novel method of key frame extraction, useful for video data. Video summarization indicates condensing the amount of data that must be examined to retrieve any noteworthy information from the video. Video summarization [1] proves to be a challenging problem as the content of video varies significantly from each other. Further significant human labor is required to manually summarize video. To tackle this issue, this paper proposes an algorithm that summarizes video without prior knowledge. Video summarization is not only useful in saving time but might represent some features which may not be caught by a human at first sight. A significant difficulty is the lack of a pre-defined dataset as well as a metric to evaluate the performance of a given algorithm. We propose a modified version of the harvesting representative frames of a video sequence for abstraction. The concept is to quantitatively measure the difference between successive frames by computing the respective statistics including mean, variation and multiple standard deviations. Then only those frames are considered that are above a predefined threshold of standard deviation. The proposed methodology is further enhanced by making it user interactive, so a user will enter the keyword about the type of frames he desires. Based on input keyword, frames are extracted from the Google Search API and compared with video frames to get desired frames.

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Authors and Affiliations

  1. Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India

    Nisha P. Shetty & Tushar Garg

Authors
  1. Nisha P. Shetty
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  2. Tushar Garg
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Corresponding author

Correspondence to Nisha P. Shetty .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. University of Malta, Valletta, Malta

    Gianluca Valentino

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Shetty, N.P., Garg, T. (2020). User Assisted Clustering Based Key Frame Extraction. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_5

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  • DOI: https://doi.org/10.1007/978-981-15-6634-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6633-2

  • Online ISBN: 978-981-15-6634-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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