Fixation prediction with a combined model of bottom-up saliency and vanishing point
This work addresses fixation prediction for computer vision and human-computer interaction, but it is incremental as it builds on existing saliency models by adding a specific cognitive factor.
The paper tackled the problem of predicting human gaze in natural scenes by incorporating vanishing point information into a saliency model, and showed that the combined model outperformed state-of-the-art saliency models on their dataset using three evaluation scores.
By predicting where humans look in natural scenes, we can understand how they perceive complex natural scenes and prioritize information for further high-level visual processing. Several models have been proposed for this purpose, yet there is a gap between best existing saliency models and human performance. While many researchers have developed purely computational models for fixation prediction, less attempts have been made to discover cognitive factors that guide gaze. Here, we study the effect of a particular type of scene structural information, known as the vanishing point, and show that human gaze is attracted to the vanishing point regions. We record eye movements of 10 observers over 532 images, out of which 319 have vanishing points. We then construct a combined model of traditional saliency and a vanishing point channel and show that our model outperforms state of the art saliency models using three scores on our dataset.