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Saliency Prediction for Visual Regions of Interest with Applications in Advertising

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Video Analytics. Face and Facial Expression Recognition and Audience Measurement (VAAM 2016, FFER 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10165))

Abstract

Human visual fixations play a vital role in a plethora of genres, ranging from advertising design to human-computer interaction. Considering saliency in images thus brings significant merits to Computer Vision tasks dealing with human perception. Several classification models have been developed to incorporate various feature levels and estimate free eye-gazes. However, for real-time applications (Here, real-time applications refer to those that are time, and often resource-constrained, requiring speedy results. It does not imply on-line data analysis), the deep convolution neural networks are either difficult to deploy, given current hardware limitations or the proposed classifiers cannot effectively combine image semantics with low-level attributes. In this paper, we propose a novel neural network approach to predict human fixations, specifically aimed at advertisements. Such analysis significantly impacts the brand value and assists in audience measurement. A dataset containing 400 print ads across 21 successful brands was used to successfully evaluate the effectiveness of advertisements and their associated fixations, based on the proposed saliency prediction model.

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Notes

  1. 1.

    Available online at http://people.csail.mit.edu/tjudd/WherePeopleLook.

  2. 2.

    The salient points shown by actual fixations, i.e., raw user inputs (not normalized), depict wagering user attention and thus, do not completely portray salient locations in the image. Instead, they are used as a rough baseline for comparison.

  3. 3.

    By effectiveness, we mean that the advertisement highlights the product, company etc. and immediately captures consumer attention, sparking interest.

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, 575025, India

    Shailee Jain & S. Sowmya Kamath

Authors
  1. Shailee Jain
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  2. S. Sowmya Kamath
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Corresponding author

Correspondence to Shailee Jain .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Kamal Nasrollahi

  2. Institute of Applied Sciences and Intelligent Systems, Lecce, Italy

    Cosimo Distante

  3. Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

  5. Aalborg University, Aalborg, Denmark

    Thomas B. Moeslund

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Rensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

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Jain, S., Sowmya Kamath, S. (2017). Saliency Prediction for Visual Regions of Interest with Applications in Advertising. In: Nasrollahi, K., et al. Video Analytics. Face and Facial Expression Recognition and Audience Measurement. VAAM FFER 2016 2016. Lecture Notes in Computer Science(), vol 10165. Springer, Cham. https://doi.org/10.1007/978-3-319-56687-0_5

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  • DOI: https://doi.org/10.1007/978-3-319-56687-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56686-3

  • Online ISBN: 978-3-319-56687-0

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