We propose a definition of saliency by considering what the visual system is trying to optimize when directing attention. The resulting model is a Bayesian framework from which bottom-up saliency emerges naturally as the self-information of visual features, and overall saliency (incorporating top-down information with bottom-up saliency) emerges as the pointwise mutual information between the features and the target when searching for a target. An implementation of our framework demonstrates that our model's bottom-up saliency maps perform as well as or better than existing algorithms in predicting people's fixations in free viewing. Unlike existing saliency measures, which depend on the statistics of the particular image being viewed, our measure of saliency is derived from natural image statistics, obtained in advance from a collection of natural images. For this reason, we call our model SUN (Saliency Using Natural statistics). A measure of saliency based on natural image statistics, rather than based on a single test image, provides a straightforward explanation for many search asymmetries observed in humans; the statistics of a single test image lead to predictions that are not consistent with these asymmetries. In our model, saliency is computed locally, which is consistent with the neuroanatomy of the early visual system and results in an efficient algorithm with few free parameters.

When people try to find particular objects in natural scenes they make extensive use of knowledge about how and where objects tend to appear in a scene. Although many forms of such "top-down" knowledge have been incorporated into saliency map models of visual search, surprisingly, the role of object appearance has been infrequently investigated. Here we present an appearance-based saliency model derived in a Bayesian framework. We compare our approach with both bottom-up saliency algorithms as well as the state-of-the-art Contextual Guidance model of Torralba et al. (2006) at predicting human fixations. Although both top-down approaches use very different types of information, they achieve similar performance; each substantially better than the purely bottomup models. Our experiments reveal that a simple model of object appearance can predict human fixations quite well, even making the same mistakes as people.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.

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The role of memory in guiding attention allocation in daily behaviors is not well understood. In experiments with two-dimensional (2D) images, there is mixed evidence about the importance of memory. Because the stimulus context in laboratory experiments and daily behaviors differs extensively, we investigated the role of memory in visual search, in both two-dimensional (2D) and three-dimensional (3D) environments. A 3D immersive virtual apartment composed of two rooms was created, and a parallel 2D visual search experiment composed of snapshots from the 3D environment was developed. Eye movements were tracked in both experiments. Repeated searches for geometric objects were performed to assess the role of spatial memory. Subsequently, subjects searched for realistic context objects to test for incidental learning. Our results show that subjects learned the room-target associations in 3D but less so in 2D. Gaze was increasingly restricted to relevant regions of the room with experience in both settings. Search for local contextual objects, however, was not facilitated by early experience. Incidental fixations to context objects do not necessarily benefit search performance. Together, these results demonstrate that memory for global aspects of the environment guides search by restricting allocation of attention to likely regions, whereas task relevance determines what is learned from the active search experience. Behaviors in 2D and 3D environments are comparable, although there is greater use of memory in 3D.