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A key property of human visual behavior is the very frequent movement of our eyes to potentially relevant information in the environment. Observers thus continuously have to prioritize information for directing their eyes to. Research in this field has been hampered by a lack of appropriate measures and tools. Here, we propose and validate a novel measure of priority that takes advantage of the variability in the natural viewing behavior of individual observers. In short, our measure assumes that priority is low when observers' gaze behavior is inconsistent and high when it is very consistent. We calculated priority for gaze data obtained during an experiment in which participants viewed dynamic natural scenes while we simultaneously recorded their gaze position and brain activity using functional magnetic resonance imaging. Our priority measure shows only limited correlation with various saliency, surprise, and motion measures, indicating it is assessing a distinct property of visual behavior. Finally, we correlated our priority measure with the BOLD signal, thereby revealing activity in a select number of human occipital and parietal areas. This suggests the presence of a cortical network involved in computing and representing viewing priority. We conclude that our new analysis method allows for empirically establishing the priority of events in near-natural vision paradigms.

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RESUMEN

We here study the predictability of eye movements when viewing high-resolution natural videos. We use three recently published gaze data sets that contain a wide range of footage, from scenes of almost still-life character to professionally made, fast-paced advertisements and movie trailers. Inter-subject gaze variability differs significantly between data sets, with variability being lowest for the professional movies. We then evaluate three state-of-the-art saliency models on these data sets. A model that is based on the invariants of the structure tensor and that combines very generic, sparse video representations with machine learning techniques outperforms the two reference models; performance is further improved for two data sets when the model is extended to a perceptually inspired colour space. Finally, a combined analysis of gaze variability and predictability shows that eye movements on the professionally made movies are the most coherent (due to implicit gaze-guidance strategies of the movie directors), yet the least predictable (presumably due to the frequent cuts). Our results highlight the need for standardized benchmarks to comparatively evaluate eye movement prediction algorithms.

RESUMEN

Since visual attention-based computer vision applications have gained popularity, ever more complex, biologically inspired models seem to be needed to predict salient locations (or interest points) in naturalistic scenes. In this paper, we explore how far one can go in predicting eye movements by using only basic signal processing, such as image representations derived from efficient coding principles, and machine learning. To this end, we gradually increase the complexity of a model from simple single-scale saliency maps computed on grayscale videos to spatiotemporal multiscale and multispectral representations. Using a large collection of eye movements on high-resolution videos, supervised learning techniques fine-tune the free parameters whose addition is inevitable with increasing complexity. The proposed model, although very simple, demonstrates significant improvement in predicting salient locations in naturalistic videos over four selected baseline models and two distinct data labeling scenarios.

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Based on the principle of efficient coding, we present a theoretical framework for how to categorize the basic types of changes that can occur in a spatio-temporal signal. First, theoretical results for the problem of estimating multiple transparent motions are reviewed. Then, confidence measures for the presence of multiple motions are used to derive a basic alphabet of local signal variation that includes motion layers. To better understand and visualize this alphabet, a representation of motions in the projective plane is used. A further, practical contribution is an interactive tool that allows generating multiple motion patterns and displaying them in various apertures. In our framework, we can explain some well-known results on coherent motion and a few more complex perceptual phenomena such as the 2D-1D entrainment effect, but the focus of this paper is on the methods. Our working hypothesis is that efficient representations can be obtained by suppressing all the redundancies that arise if the visual input does not change in a particular direction, or a set of directions. Finally, we assume that human eye movements will tend to avoid the redundant parts of the visual input and report results where our framework has been used to obtain very good predictions of eye movements made on overlaid natural videos.

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RESUMEN

We deal with the analysis of eye movements made on natural movies in free-viewing conditions. Saccades are detected and used to label two classes of movie patches as attended and non-attended. Machine learning techniques are then used to determine how well the two classes can be separated, i.e., how predictable saccade targets are. Although very simple saliency measures are used and then averaged to obtain just one average value per scale, the two classes can be separated with an ROC score of around 0.7, which is higher than previously reported results. Moreover, predictability is analysed for different representations to obtain indirect evidence for the likelihood of a particular representation. It is shown that the predictability correlates with the local intrinsic dimension in a movie.

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