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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
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Introduction

Attention involves selectively processing certain aspects of theenvironment while ignoring others.


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Introduction


Crucial for real time processing of the stimuli


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Introduction



Two aspects of visual attention:


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Introduction




- Bottom up- Top down


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Introduction




- Bottom up- Top down

The aim is to model the top down aspect.


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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


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Object Based Model

Objects are the most salient entities in the scene for taskdriven 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,


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Object Based Model


O = the series of attended objects.


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Object Based Model



V =(m;),where m represents the structure of the net and includes the state transition matrix and the observationmatrix


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Object Based Model



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


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Object Based Model



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


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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


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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.


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