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INNS Conference on Big Data

INNS 2016: Advances in Big Data pp 18–28Cite as

Compact Video Description and Representation for Automated Summarization of Human Activities

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 529))

Abstract

A compact framework is presented for the description and representation of videos depicting human activities, with the goal of enabling automated large-volume video summarization for semantically meaningful key-frame extraction. The framework is structured around the concept of per-frame visual word histograms, using the popular Bag-of-Features approach. Three existing image descriptors (histogram, FMoD, SURF) and a novel one (LMoD), as well as a component of an existing state-of-the-art activity descriptor (Dense Trajectories), are adapted into the proposed framework and quantitatively compared against each other, as well as against the most common video summarization descriptor (global image histogram), using a publicly available annotated dataset and the most prevalent video summarization method, i.e., frame clustering. In all cases, several image modalities are exploited (luminance, hue, edges, optical flow magnitude) in order to simultaneously capture information about the depicted shapes, colors, lighting, textures and motions. The quantitative evaluation results indicate that one of the proposed descriptors clearly outperforms the competing approaches in the context of the presented framework.

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 316564 (IMPART).

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Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Ioannis Mademlis, Anastasios Tefas, Nikos Nikolaidis & Ioannis Pitas

Authors
  1. Ioannis Mademlis
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  2. Anastasios Tefas
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  3. Nikos Nikolaidis
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  4. Ioannis Pitas
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Corresponding author

Correspondence to Ioannis Mademlis .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University , Lancaster, United Kingdom

    Plamen Angelov

  2. Data Engineering Lab, Dept. of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  3. Lab of Forest Informatics (FiLAB), Democritus University of Thrace , Orestiada, Greece

    Lazaros Iliadis

  4. WPC Information Systems Faculty, Arizona State University , Tempe, Arizona, USA

    Asim Roy

  5. Electrical Engineering Dept, (ICA), Pontifical Catholic Univ of Rio de Janei , Rio de Janeiro, Rio de Janeiro, Brazil

    Marley Vellasco

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Mademlis, I., Tefas, A., Nikolaidis, N., Pitas, I. (2017). Compact Video Description and Representation for Automated Summarization of Human Activities. In: Angelov, P., Manolopoulos, Y., Iliadis, L., Roy, A., Vellasco, M. (eds) Advances in Big Data. INNS 2016. Advances in Intelligent Systems and Computing, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-47898-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-47898-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47897-5

  • Online ISBN: 978-3-319-47898-2

  • eBook Packages: EngineeringEngineering (R0)

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