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Dynamic Saliency Models and Human Attention: A Comparative Study on Videos

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7726))

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Abstract

Significant progress has been made in terms of computational models of bottom-up visual attention (saliency). However, efficient ways of comparing these models for still images remain an open research question. The problem is even more challenging when dealing with videos and dynamic saliency. The paper proposes a framework for dynamic-saliency model evaluation, based on a new database of diverse videos for which eye-tracking data has been collected. In addition, we present evaluation results obtained for 4 state-of-the-art dynamic-saliency models, two of which have not been verified on eye-tracking data before.

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Author information

Authors and Affiliations

  1. University of Mons, Belgium

    Nicolas Riche, Matei Mancas, Dubravko Culibrk, Vladimir Crnojevic, Bernard Gosselin & Thierry Dutoit

  2. University of Novi Sad, Serbia

    Nicolas Riche, Matei Mancas, Dubravko Culibrk, Vladimir Crnojevic, Bernard Gosselin & Thierry Dutoit

Authors
  1. Nicolas Riche
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  2. Matei Mancas
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  3. Dubravko Culibrk
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  4. Vladimir Crnojevic
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  5. Bernard Gosselin
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  6. Thierry Dutoit
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Riche, N., Mancas, M., Culibrk, D., Crnojevic, V., Gosselin, B., Dutoit, T. (2013). Dynamic Saliency Models and Human Attention: A Comparative Study on Videos. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_45

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  • DOI: https://doi.org/10.1007/978-3-642-37431-9_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37430-2

  • Online ISBN: 978-3-642-37431-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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