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An Automated High-Level Saliency Predictor for Smart Game Balancing

Koulieris, George Alex; Drettakis, George; Cunningham, Douglas; Mania, Katerina

An Automated High-Level Saliency Predictor for Smart Game Balancing Thumbnail


George Drettakis

Douglas Cunningham

Katerina Mania


Successfully predicting visual attention can significantly improve many aspects of computer graphics: scene design, interactivity and rendering. Most previous attention models are mainly based on low-level image features, and fail to take into account high-level factors such as scene context, topology, or task. Low-level saliency has previously been combined with task maps, but only for predetermined tasks. Thus, the application of these methods to graphics (e.g., for selective rendering) has not achieved its full potential. In this article, we present the first automated high-level saliency predictor incorporating two hypotheses from perception and cognitive science that can be adapted to different tasks. The first states that a scene is comprised of objects expected to be found in a specific context as well objects out of context which are salient (scene schemata) while the other claims that viewer’s attention is captured by isolated objects (singletons). We propose a new model of attention by extending Eckstein’s Differential Weighting Model. We conducted a formal eye-tracking experiment which confirmed that object saliency guides attention to specific objects in a game scene and determined appropriate parameters for a model. We present a GPU-based system architecture that estimates the probabilities of objects to be attended in real- time. We embedded this tool in a game level editor to automatically adjust game level difficulty based on object saliency, offering a novel way to facilitate game design. We perform a study confirming that game level completion time depends on object topology as predicted by our system.


Koulieris, G. A., Drettakis, G., Cunningham, D., & Mania, K. (2015). An Automated High-Level Saliency Predictor for Smart Game Balancing. ACM Transactions on Applied Perception, 11(4), Article 17.

Journal Article Type Article
Acceptance Date Jun 6, 2014
Online Publication Date Dec 8, 2014
Publication Date Jan 9, 2015
Deposit Date Jun 5, 2018
Publicly Available Date Jul 24, 2018
Journal ACM Transactions on Applied Perception
Print ISSN 1544-3558
Electronic ISSN 1544-3965
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 11
Issue 4
Article Number 17


Accepted Journal Article (6.8 Mb)

Copyright Statement
© ACM 2014. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Applied Perception (TAP),

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