Scanpath modeling and classification with Hidden Markov Models

How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification. This method relies on variational hidden Markov models (HMMs) and discriminant analysis (DA). HMMs encapsulate the dynamic and individualistic dimensions of gaze behavior, allowing DA to capture systematic patterns diagnostic of a given class of observers and/or stimuli. We test our approach on two very different datasets. Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the number of salient regions present in the stimuli. Secondly, we use eye positions recorded while viewing 15 conversational videos, and infer a stimulus-related characteristic: the presence or absence of original soundtrack. We achieve an average 81.2% correct classification rate (chance = 50%). HMMs allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior. This synergistic approach between behavior and machine learning will open new avenues for simple quantification of gazing behavior. We release SMAC with HMM, a Matlab toolbox freely available to the community under an open-source license agreement.published_or_final_versio

Longer fixation duration while viewing face images

The spatio-temporal properties of saccadic eye movements can be influenced by the cognitive demand and the characteristics of the observed scene. Probably due to its crucial role in social communication, it is argued that face perception may involve different cognitive processes compared with non-face object or scene perception. In this study, we investigated whether and how face and natural scene images can influence the patterns of visuomotor activity. We recorded monkeys’ saccadic eye movements as they freely viewed monkey face and natural scene images. The face and natural scene images attracted similar number of fixations, but viewing of faces was accompanied by longer fixations compared with natural scenes. These longer fixations were dependent on the context of facial features. The duration of fixations directed at facial contours decreased when the face images were scrambled, and increased at the later stage of normal face viewing. The results suggest that face and natural scene images can generate different patterns of visuomotor activity. The extra fixation duration on faces may be correlated with the detailed analysis of facial features

Audiovisual Saliency Prediction in Uncategorized Video Sequences based on Audio-Video Correlation

Substantial research has been done in saliency modeling to develop intelligent machines that can perceive and interpret their surroundings. But existing models treat videos as merely image sequences excluding any audio information, unable to cope with inherently varying content. Based on the hypothesis that an audiovisual saliency model will be an improvement over traditional saliency models for natural uncategorized videos, this work aims to provide a generic audio/video saliency model augmenting a visual saliency map with an audio saliency map computed by synchronizing low-level audio and visual features. The proposed model was evaluated using different criteria against eye fixations data for a publicly available DIEM video dataset. The results show that the model outperformed two state-of-the-art visual saliency models.Comment: 9 pages, 2 figures, 4 table

Semantic content outweighs low-level saliency in determining children's and adults' fixation of movies

To make sense of the visual world, we need to move our eyes to focus regions of interest on the high-resolution fovea. Eye movements, therefore, give us a way to infer mechanisms of visual processing and attention allocation. Here, we examined age-related differences in visual processing by recording eye movements from 37 children (aged 6–14 years) and 10 adults while viewing three 5-min dynamic video clips taken from child-friendly movies. The data were analyzed in two complementary ways: (a) gaze based and (b) content based. First, similarity of scanpaths within and across age groups was examined using three different measures of variance (dispersion, clusters, and distance from center). Second, content-based models of fixation were compared to determine which of these provided the best account of our dynamic data. We found that the variance in eye movements decreased as a function of age, suggesting common attentional orienting. Comparison of the different models revealed that a model that relies on faces generally performed better than the other models tested, even for the youngest age group (<10 years). However, the best predictor of a given participant’s eye movements was the average of all other participants’ eye movements both within the same age group and in different age groups. These findings have implications for understanding how children attend to visual information and highlight similarities in viewing strategies across development

A Survey of Visual Attention Models

The present paper surveys visual attention models, showing factors’ categorization. It also studies bottom-up models in comparison to top-to-down, spatial models compared to spatial-temporal ones, obvious attention against the hidden one, and space-based models against the object-based ones. It categorizes some challenging model issues, including biological calculations, correlation with the set of eye-movement data, as well as bottom-up and top-to-down topics, explaining each in details