An Automated High-Level Saliency Predictor for Smart Game Balancing

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

Abstract

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.

Citation

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. https://doi.org/10.1145/2637479

• 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
• DOI: https://doi.org/10.1145/2637479
• Public URL: https://durham-repository.worktribe.com/output/1329524