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Review of Methods to Predict Social Image Interestingness and Memorability

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9256))

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Abstract

An entire industry has developed around keyword optimization for ad buyers. However, social media landscape has shift to a photo driven behavior and there is a need to overcome the challenge to analyze all this large amount of visual data that users post in internet. We will address this analysis by providing a review on how to measure image and video interestingness and memorability from content that is tacked spontaneously in social networks. We will investigate current state-of-the-art of methods analyzing social media images and provide further research directions that could be beneficial for both, users and companies.

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Authors and Affiliations

  1. DEEEA, Centre Easy, Agents Research LAB, Universitat de Giroan, Girona, Spain

    Xesca Amengual, Anna Bosch & Josep Lluís de la Rosa

Authors
  1. Xesca Amengual
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  2. Anna Bosch
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  3. Josep Lluís de la Rosa
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Corresponding author

Correspondence to Xesca Amengual .

Editor information

Editors and Affiliations

  1. University of Malta, Msida, Malta

    George Azzopardi

  2. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Amengual, X., Bosch, A., de la Rosa, J.L. (2015). Review of Methods to Predict Social Image Interestingness and Memorability. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-23192-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23191-4

  • Online ISBN: 978-3-319-23192-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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