@conference {Borji_etal11bmvc,
	title = {Computational Modeling of Top-down Visual Attention in Interactive Environments},
	booktitle = {Proc. British Machine Vision Conference (BMVC 2011)},
	year = {2011},
	month = {Sep},
	abstract = {<p>Modeling how visual saliency guides the deployment of attention over visual scenes has attracted much interest recently - among both computer vision and experimental/computational researchers - since visual attention is a key function of both machine and biological vision systems. Research efforts in computer vision have mostly been focused on modeling bottom-up saliency. Strong influences on attention and eye movements, however, come from instantaneous task demands. Here, we propose models of top-down visual guidance considering task influences. The new models estimate the state of a human subject performing a task (here, playing video games), and map that state to an eye position. Factors influencing state come from scene gist, physical actions, events, and bottom-up saliency. Proposed models fall into two categories. In the first category, we use classical discriminative classifiers, including Regres- sion, kNN and SVM. In the second category, we use Bayesian Networks to combine all the multi-modal factors in a unified framework. Our approaches significantly outperform 15 competing bottom-up and top-down attention models in predicting future eye fixations.</p>},
	author = {Ali Borji and Dicky N. Sihite and Laurent Itti}
}