Saliency prediction with scene structural guidance

Previous works have suggested the role of scene information in directing gaze. The structure of a scene provides global contextual information that complements local object information in saliency prediction. In this study, we explore how scene envelopes such as openness, depth, and perspective affect visual attention in natural outdoor images. To facilitate this study, an eye tracking dataset is first built with 500 natural scene images and eye tracking data with 15 subjects free-viewing the images. We make observations on scene layout properties and propose a set of scene structural features relating to visual attention. We further integrate features from deep neural networks and use the set of complementary features for saliency prediction. Our features are independent of and can work together with many computational modules, and this work demonstrates the use of Multiple kernel learning (MKL) as an example to integrate the features at low- and high-levels. Experimental results demonstrate that our model outperforms existing methods and our scene structural features can improve the performance of other saliency models in outdoor scenes.