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Databases for Saliency Model Evaluation

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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))

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Abstract

The comparison between saliency algorithms needs two prerequisites: a dataset of stimuli with a ground truth on which the algorithms can be compared and a metric which measures in an objective way how close an algorithm and the ground truth are. This chapter focuses on the stimuli datasets and the ground truths. In computer vision, the databases for the modeling of visual attention contain two kinds of ground truth: eye movement recording and salient region labeling. The stimuli are still images or videos. A review of the main databases is achieved and more details are given for the most used among them. Stimuli differences are also shown.

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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 .

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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). Databases for Saliency Model Evaluation. 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_11

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