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Study of Parameters Affecting Visual Saliency Assessment

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From Human Attention to Computational Attention

Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS,volume 10))

Abstract

The computational modelling of visual attention has been developed and expanded considerably during the past 10 years. Many different saliency models are now available online (for still images and videos). At the same time, many popular image-video datasets with human gaze data or binary masks have been released to evaluate saliency models with commonly used evaluation metrics. The new challenges and future directions for this field are therefore to establish evaluation protocols and saliency benchmarks. Although some evaluation studies and online benchmarks have already been proposed and are major contributions, a key underlying issue is: how can one fairly evaluate all these models? In this chapter, we investigate this question with an evaluation, divided into four experiments, leading to the proposition of a new evaluation framework.

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Author information

Authors and Affiliations

  1. Numediart Institute, University of Mons (UMONS), 31, Bd. Dolez, 7000, Mons, Belgium

    Nicolas Riche

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  1. Nicolas Riche
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Correspondence to Nicolas Riche .

Editor information

Editors and Affiliations

  1. Numediart Institute, University of Mons, Mons, Belgium

    Matei Mancas

  2. Columbia University, New York, New York, USA

    Vincent P. Ferrera

  3. Numediart Institute, University of Mons, MONS, Belgium

    Nicolas Riche

  4. King's College, LONDON, United Kingdom

    John G. Taylor

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Riche, N. (2016). Study of Parameters Affecting Visual Saliency Assessment. In: Mancas, M., Ferrera, V., Riche, N., Taylor, J. (eds) From Human Attention to Computational Attention. Springer Series in Cognitive and Neural Systems, vol 10. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3435-5_13

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