Markov chain based computational visual attention model that learns from eye tracking data

Ma, Zhijun; Zhao, Xinbo; Zou, Xiaochun; Wang, James; Wang, Wenhu

doi:10.1016/j.patrec.2014.06.002

Cited by 16 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we used novel data-mining methods that encapsulate the highly dynamic and individualistic spatiotemporal nature of gaze. Although a few previous studies have used Markov-based analysis with eyetracking data to identify fixations and saccades (Salvucci & Goldberg, 2000), to infer observers' tasks (Haji-Abolhassani & Clark, 2014;Simola et al, 2008), or to build visual saliency models (Zhong, Zhao, Zou, Wang, & Wang, 2014), only a small number of recent studies have applied these techniques to face exploration (Chuk et al, 2014;Kanan et al, 2015). This approach is particularly powerful as faces feature very clear and stable ROIs (eyes, mouth, nose), allowing meaningful comparisons of Markov model states across stimuli and observers.…”

Section: Discussionmentioning

confidence: 99%

Face exploration dynamics differentiate men and women

et al. 2016

View full text Add to dashboard Cite

The human face is central to our everyday social interactions. Recent studies have shown that while gazing at faces, each one of us has a particular eye-scanning pattern, highly stable across time. Although variables such as culture or personality have been shown to modulate gaze behavior, we still don't know what shapes these idiosyncrasies. Moreover, most previous observations rely on static analyses of small-sized eye-position data sets averaged across time. Here, we probe the temporal dynamics of gaze to explore what information can be extracted about the observers and what is being observed. Controlling for any stimuli effect, we demonstrate that among many individual characteristics, the gender of both the participant (gazer) and the person being observed (actor) are the factors that most influence gaze patterns during face exploration. We record and exploit the largest set of eye-tracking data (405 participants, 58 nationalities) from participants watching videos of another person. Using novel data-mining techniques, we show that female gazers follow a much more exploratory scanning strategy than males. Moreover, female gazers watching female actresses look more at the eye on the left side. These results have strong implications in every field using gaze-based models from computer vision to clinical psychology.

show abstract

Section: Discussionmentioning

confidence: 99%

Face exploration dynamics differentiate men and women

et al. 2016

View full text Add to dashboard Cite

show abstract

“…In order to study the cognitive aspect of the Human Visual System, one of the common approaches is to build a mathematical model that sufficiently represents the system (Simola et al, 2008;Zhong et al, 2014). Among the most appropriate is the NN model, which is a simplified brain model.…”

Section: Methodsmentioning

confidence: 99%

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

Moch¹,

Komarudin

Susilo

2017

IJTech

View full text Add to dashboard Cite

Fixation points, as the stopping location of eye movements, can be extracted to generate valuable information about a picture or an object. This information is valuable as it enables the identification of the area/part of the picture that attracts people's attention, which can be used as a consideration when making decisions in the future, for example in marketing. For this reason, in this study, a Neural Network (NN) model was developed to predict the fixation points of a picture. Specifically, the authors experimented with various transfer and training functions in the NN in order to determine which causes the fewest errors. The results show that the method used is applicable in practice since it produces MAPE (Mean Absolute Percent Error) of around 13-15% and MSE (Mean Squared Error) of 0.9-1.1%.

show abstract

“…(3) Determine whether all of the features channel have already been selected, and if so, turn (5), otherwise, select the task targets of key features queue the next major key corresponding channel, turn (4). (4) Calculate the activity diagram, with the previously calculated features channel activity diagram together to weight and generate saliency map (where the weighting coefficient by the task targets and interference target feature data on this feature difference normalized results are given), turn (2); (5) Not hit the target, the algorithm fails; (6) Hit the target, task is completed.…”

Section: Extracting Visual Attention Features Of Knownmentioning

confidence: 99%

“…Zhong use the transition probability SVR prediction Markov chain of eye tracking data to estimate generating the saliency map by the stationary distribution of Markov chain [4]. Mahadevan is use of bottom-up center-surround salient information, the feature selection of feature-based attention and top-down saliency of target detection to track the salient target [5].Han extract the object bank and other features of the input image and calculating entropy to guide image classification tasks [6].…”

Section: Introductionmentioning

confidence: 99%

The Top-down Visual Attention Method with Known Interference Target

Wang¹,

Tian²,

Ke³

2017

dtcse

View full text Add to dashboard Cite

The simulation of visual attention can improve the efficiency of relevant intelligent applications by finding out most salient locations quickly in images or videos while ignoring redundant information. The paper can reason key features of target based on task scenario, filter the bottom-up visual attention feature channel by top-down method, and propose the top-down visual attention method with known interference target. The proposed method can minimize the time of generating visual saliency map by ensuring the hit rate of mission targets, and improve the efficiency of the search targets based on visual attention.

show abstract

Markov chain based computational visual attention model that learns from eye tracking data

Cited by 16 publications

References 29 publications

Face exploration dynamics differentiate men and women

Face exploration dynamics differentiate men and women

Development of Eye Fixation Points Prediction Model from Eye Tracking Data using Neural Network

The Top-down Visual Attention Method with Known Interference Target

Contact Info

Product

Resources

About