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By predicting where humans look in natural scenes, we can understand how theyperceive complex natural scenes and prioritize information for furtherhigh-level visual processing. Several models have been proposed for thispurpose, yet there is a gap between best existing saliency models and humanperformance. While many researchers have developed purely computational modelsfor fixation prediction, less attempts have been made to discover cognitivefactors that guide gaze. Here, we study the effect of a particular type ofscene structural information, known as the vanishing point, and show that humangaze is attracted to the vanishing point regions. We record eye movements of 10observers over 532 images, out of which 319 have vanishing points. We thenconstruct a combined model of traditional saliency and a vanishing pointchannel and show that our model outperforms state of the art saliency modelsusing three scores on our dataset.

Fixation prediction with a combined model of bottom-up saliency and vanishing point