Dynamic Markov random fields for stochastic modeling of visual attention

Kimura, Atsushi; Pang, Derek; Takeuchi, Tatsuto; Yamato, Junji; Kashino, Kunio

doi:10.1109/icpr.2008.4761025

Cited by 12 publications

(14 citation statements)

References 11 publications

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“…As pointed out from the beginning, scanpath variability has been abundantly overlooked in the current literature (cfr., [4]), but there are few notable exceptions. In [61] simple eye-movements patterns, in the vein of [19], are straightforwardly incorporated as a prior of a dynamic Bayesian network to guide the sequence of eye focusing positions on videos. The model presented in [62] embeds at least one parameter suitable to be tuned to obtain different saccade length distributions on static images, although statistics obtained by varying such parameter are still far from those of human data.…”

Section: Discussionmentioning

confidence: 99%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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Abstract-Visual attention guides our gaze to relevant parts of the viewed scene, yet the moment-to-moment relocation of gaze can be different among observers even though the same locations are taken into account. Surprisingly, the variability of eye movements has been so far overlooked by the great majority of computational models of visual attention. In this paper we present the ecological sampling model, a stochastic model of eye guidance explaining such variability. The gaze shift mechanism is conceived as an active random sampling that the foraging eye carries out upon the visual landscape, under the constraints set by the observable features and the global complexity of the landscape. By drawing on results reported in the foraging literature, the actual gaze relocation is eventually driven by a stochastic differential equation whose noise source is sampled from a mixture of α-stable distributions. This way, the sampling strategy proposed here allows to mimic a fundamental property of the eye guidance mechanism: where we choose to look next at any given moment in time, it is not completely deterministic, but neither is it completely random To show that the model yields gaze shift motor behaviors that exhibit statistics similar to those displayed by human observers, we compare simulation outputs with those obtained from eye-tracked subjects while viewing complex dynamic scenes.

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Section: Discussionmentioning

confidence: 99%

Ecological Sampling of Gaze Shifts

Boccignone

Ferraro

2014

IEEE Trans. Cybern.

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“…Models of visuospatial attention, in particular of the functioning and construction of the saliency map, indeed include non-deterministic components [ 17 ]. They emphasize that, for example, stochastic fluctuations of the saliency of particular objects or features, aid in the functional modelling of the visual system [ 19 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Spontaneous Alpha Power Lateralization Predicts Detection Performance in an Un-Cued Signal Detection Task

et al. 2016

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Focusing one’s attention by external guiding stimuli towards a specific area of the visual field produces systematical neural signatures. One of the most robust is the change in topological distribution of oscillatory alpha band activity across parieto-occipital cortices. In particular, decreases in alpha activity over contralateral and/or increases over ipsilateral scalp sites, respect to the side of the visual field where attention was focused. This evidence comes mainly from experiments where an explicit cue informs subjects where to focus their attention, thus facilitating detection of an upcoming target stimulus. However, recent theoretical models of attention have highlighted a stochastic or non-deterministic component related to visuospatial attentional allocation. In an attempt to evidence this component, here we analyzed alpha activity in a signal detection paradigm in the lack of informative cues; in the absence of preceding information about the location (and time) of appearance of target stimuli. We believe that the unpredictability of this situation could be beneficial for unveiling this component. Interestingly, although total alpha power did not differ between Seen and Unseen conditions, we found a significant lateralization of alpha activity over parieto-occipital electrodes, which predicted behavioral performance. This effect had a smaller magnitude compared to paradigms in which attention is externally guided (cued). However we believe that further characterization of this spontaneous component of attention is of great importance in the study of visuospatial attentional dynamics. These results support the presence of a spontaneous component of visuospatial attentional allocation and they advance pre-stimulus alpha-band lateralization as one of its neural signatures.

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“…In [65,66] simple eye-movements patterns are incorporated as a prior of a Dynamic Bayesian Network to guide the sequence of eye focusing positions on videos. The model presented in [67] embeds at least one parameter suitable to be tuned to obtain different saccade length distributions on static images.…”

Section: Discussionmentioning

confidence: 99%

Gaze shift behavior on video as composite information foraging

Boccignone

Ferraro

2013

Signal Processing: Image Communication

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a b s t r a c tThe ability to predict, given an image or a video, where a human might fixate elements of a viewed scene has long been of interest in the vision community. However, one point that is not addressed by the great majority of computational models is the variability exhibited by different observers when viewing the same scene, or even by the same subject along different trials. Here we present a model of gaze shift behavior which is driven by a composite foraging strategy operating over a time varying visual landscape and accounts for such variability.The system performs a deterministic walk if in a neighborhood of the current position of the gaze there exists a point of sufficiently high saliency; otherwise the search is driven by a Langevin equation whose random term is generated by an a-stable distribution.Results of the simulations on complex videos from the publicly available University of South California CRCNS eye-1 dataset are compared with eye-tracking data and show that the model yields gaze shift motor behaviors that exhibits statistics similar to those exhibited by human observers.

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Dynamic Markov random fields for stochastic modeling of visual attention

Cited by 12 publications

References 11 publications

Ecological Sampling of Gaze Shifts

Ecological Sampling of Gaze Shifts

Spontaneous Alpha Power Lateralization Predicts Detection Performance in an Un-Cued Signal Detection Task

Gaze shift behavior on video as composite information foraging

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