Data-driven approach to dynamic visual attention modelling

Dubravko Culibrk; Srdjan Sladojevic; Nicolas Riche; Matei Mancas; Vladimir Crnojevic

doi:10.1117/12.923559

30 April 2012 Data-driven approach to dynamic visual attention modelling

Dubravko Culibrk, Srdjan Sladojevic, Nicolas Riche, Matei Mancas, Vladimir Crnojevic

Proceedings Volume 8436, Optics, Photonics, and Digital Technologies for Multimedia Applications II; 84360N (2012) https://doi.org/10.1117/12.923559
Event: SPIE Photonics Europe, 2012, Brussels, Belgium

Abstract

Visual attention deployment mechanisms allow the Human Visual System to cope with an overwhelming amount of visual data by dedicating most of the processing power to objects of interest. The ability to automatically detect areas of the visual scene that will be attended to by humans is of interest for a large number of applications, from video coding, video quality assessment to scene understanding. Due to this fact, visual saliency (bottom-up attention) models have generated significant scientific interest in recent years. Most recent work in this area deals with dynamic models of attention that deal with moving stimuli (videos) instead of traditionally used still images. Visual saliency models are usually evaluated against ground-truth eye-tracking data collected from human subjects. However, there are precious few recently published approaches that try to learn saliency from eyetracking data and, to the best of our knowledge, no approaches that try to do so when dynamic saliency is concerned. The paper attempts to fill this gap and describes an approach to data-driven dynamic saliency model learning. A framework is proposed that enables the use of eye-tracking data to train an arbitrary machine learning algorithm, using arbitrary features derived from the scene. We evaluate the methodology using features from a state-of-the art dynamic saliency model and show how simple machine learning algorithms can be trained to distinguish between visually salient and non-salient parts of the scene.

Citation Download Citation

Dubravko Culibrk, Srdjan Sladojevic, Nicolas Riche, Matei Mancas, and Vladimir Crnojevic "Data-driven approach to dynamic visual attention modelling", Proc. SPIE 8436, Optics, Photonics, and Digital Technologies for Multimedia Applications II, 84360N (30 April 2012); https://doi.org/10.1117/12.923559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available