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What Do Datasets Say About Saliency Models?

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Book cover Pattern Recognition and Image Analysis (IbPRIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10255))

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Abstract

Given the amount and variety of saliency models, the knowledge of their pros and cons, the applications they are more suitable for, or which are the more challenging scenes for each of them, would be very useful for the progress in the field. This assessment can be done based on the link between algorithms and public datasets. In one hand, performance scores of algorithms can be used to cluster video samples according to the pattern of difficulties they pose to models. In the other hand, cluster labels can be combined with video annotations to select discriminant attributes for each cluster. In this work we seek this link and try to describe each cluster of videos in a few words.

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Acknowledgements

This work has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF).

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Xosé M. Pardo & Xosé R. Fdez-Vidal

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  1. Xosé M. Pardo
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  2. Xosé R. Fdez-Vidal
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Correspondence to Xosé M. Pardo .

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Editors and Affiliations

  1. Universidade da Beira Interior , Covilhã, Portugal

    Luís A. Alexandre

  2. University Jaume I , Castellón, Spain

    José Salvador Sánchez

  3. University of the Algarve , Faro, Portugal

    João M. F. Rodrigues

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Pardo, X.M., Fdez-Vidal, X.R. (2017). What Do Datasets Say About Saliency Models?. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-58838-4_12

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-58838-4

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