Learned saliency transformations for gaze guidance

Eleonora Vig; Michael Dorr; Erhardt Barth

doi:10.1117/12.876377

2 February 2011 Learned saliency transformations for gaze guidance

Eleonora Vig, Michael Dorr, Erhardt Barth

Proceedings Volume 7865, Human Vision and Electronic Imaging XVI; 78650W (2011) https://doi.org/10.1117/12.876377
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

The saliency of an image or video region indicates how likely it is that the viewer of the image or video fixates that region due to its conspicuity. An intriguing question is how we can change the video region to make it more or less salient. Here, we address this problem by using a machine learning framework to learn from a large set of eye movements collected on real-world dynamic scenes how to alter the saliency level of the video locally. We derive saliency transformation rules by performing spatio-temporal contrast manipulations (on a spatio-temporal Laplacian pyramid) on the particular video region. Our goal is to improve visual communication by designing gaze-contingent interactive displays that change, in real time, the saliency distribution of the scene.

Citation Download Citation

Eleonora Vig, Michael Dorr, and Erhardt Barth "Learned saliency transformations for gaze guidance", Proc. SPIE 7865, Human Vision and Electronic Imaging XVI, 78650W (2 February 2011); https://doi.org/10.1117/12.876377

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