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A Computational Model for Top Down visual
attention
Abhijit Sharang
Computer Science and Engineering
IIT Kanpur
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
7/27/2019 Cogntive Science eye fixation
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Introduction
Attention involves selectively processing certain aspects of theenvironment while ignoring others.
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
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Introduction
Attention involves selectively processing certain aspects of theenvironment while ignoring others.
Crucial for real time processing of the stimuli
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
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Introduction
Attention involves selectively processing certain aspects of theenvironment while ignoring others.
Crucial for real time processing of the stimuli
Two aspects of visual attention:
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
7/27/2019 Cogntive Science eye fixation
5/15
Introduction
Attention involves selectively processing certain aspects of theenvironment while ignoring others.
Crucial for real time processing of the stimuli
Two aspects of visual attention:
- Bottom up- Top down
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
7/27/2019 Cogntive Science eye fixation
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Introduction
Attention involves selectively processing certain aspects of theenvironment while ignoring others.
Crucial for real time processing of the stimuli
Two aspects of visual attention:
- Bottom up- Top down
The aim is to model the top down aspect.
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
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Related work
The sequential nature of attention has been exploited todevelop HMM based models for specific tasks like sandwichmaking[2].
Peters and Itti[3] developed a spatial attention model byassociating the global scene features to gaze fixation innavigation and exploration tasks
Cagli et al.[4] proposed a Bayesian framework forsensory-motor coordination in drawing tasks
Global scene features were also used by Torralba et al.[5] todevelop a discriminative model for top down saliency
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
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Object Based Model
Objects are the most salient entities in the scene for taskdriven attention
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
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Object Based Model
Objects are the most salient entities in the scene for taskdriven attentionThe sequential nature is exploited here in form of a DynamicBayesian Network to maximise the probability P(O|V) where,
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
7/27/2019 Cogntive Science eye fixation
10/15
Object Based Model
Objects are the most salient entities in the scene for taskdriven attentionThe sequential nature is exploited here in form of a DynamicBayesian Network to maximise the probability P(O|V) where,
O = the series of attended objects.
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
7/27/2019 Cogntive Science eye fixation
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Object Based Model
Objects are the most salient entities in the scene for taskdriven attentionThe sequential nature is exploited here in form of a DynamicBayesian Network to maximise the probability P(O|V) where,
O = the series of attended objects.
V =(m;),where m represents the structure of the net and includes the state transition matrix and the observationmatrix
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://goforward/http://find/http://goback/ -
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Object Based Model
Objects are the most salient entities in the scene for taskdriven attentionThe sequential nature is exploited here in form of a DynamicBayesian Network to maximise the probability P(O|V) where,
O = the series of attended objects.
V =(m;),where m represents the structure of the net and includes the state transition matrix and the observationmatrixThe model consists of the joint probability distributionP(X1:T, Y1:T, F
1:j1:T),where
X = sequence of attended spatial locationsY = sequence of attended objectsF = vector of the features associated with the objects
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/http://goback/ -
7/27/2019 Cogntive Science eye fixation
13/15
Object Based Model
Objects are the most salient entities in the scene for taskdriven attentionThe sequential nature is exploited here in form of a DynamicBayesian Network to maximise the probability P(O|V) where,
O = the series of attended objects.
V =(m;),where m represents the structure of the net and includes the state transition matrix and the observationmatrixThe model consists of the joint probability distributionP(X1:T, Y1:T, F
1:j1:T),where
X = sequence of attended spatial locationsY = sequence of attended objectsF = vector of the features associated with the objects
Exploit some property independences to reduce theformulation
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
http://find/ -
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Experimentation
Dataset consists of eye tracking data of participants engagedin playing video games.[1]
Developing relevant features for the model.Apart from DBN,an equivalent model based on HMM orKalman filter is intended to be developed for comparisonpurpose.
Comparison also intended to be done with the classifier basedmodels
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
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References
1 Borji, Ali, Dicky N. Sihite, and Laurent Itti. An Object-BasedBayesian Framework for Top-Down Visual Attention. Twenty-SixthAAAI Conference on Artificial Intelligence. 2012
2 WEILIE, YI, and Dana Ballard. Recognizing behavior in hand-eyecoordination patterns. International Journal of Humanoid Robotics6.03 (2009): 337-359
3 Peters, Robert J., and Laurent Itti. Beyond bottom-up:Incorporating task-dependent influences into a computational modelof spatial attention. Computer Vision and Pattern Recognition,2007. CVPR07. IEEE Conference on. IEEE, 2007.
4 Coen-Cagli, Ruben, et al. Visuomotor characterization of eyemovements in a drawing task. Vision research 49.8 (2009):810-818.
5 Oliva, Aude, and Antonio Torralba. The role of context in objectrecognition. Trends in cognitive sciences 11.12 (2007): 520-527.
Abhijit Sharang Computer Science and Engineering IIT Kanpur
A Computational Model for Top Down visual attention
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