Please use this identifier to cite or link to this item: https://doi.org/10.1167/14.1.28
Title: Predicting human gaze beyond pixels
Authors: Xu, J.
Jiang, M.
Wang, S.
Kankanhalli, M.S. 
Zhao, Q. 
Keywords: Computational model
Dataset
Object saliency
Saliency attribute
Semantic saliency
Visual saliency
Issue Date: 2014
Citation: Xu, J., Jiang, M., Wang, S., Kankanhalli, M.S., Zhao, Q. (2014). Predicting human gaze beyond pixels. Journal of Vision 14 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1167/14.1.28
Abstract: A large body of previous models to predict where people look in natural scenes focused on pixel-level image attributes. To bridge the semantic gap between the predictive power of computational saliency models and human behavior, we propose a new saliency architecture that incorporates information at three layers: pixel-level image attributes, object-level attributes, and semanticlevel attributes. Object- and semantic-level information is frequently ignored, or only a few sample object categories are discussed where scaling to a large number of object categories is not feasible nor neurally plausible. To address this problem, this work constructs a principled vocabulary of basic attributes to describe object- and semantic-level information thus not restricting to a limited number of object categories. We build a new dataset of 700 images with eye-tracking data of 15 viewers and annotation data of 5,551 segmented objects with fine contours and 12 semantic attributes (publicly available with the paper). Experimental results demonstrate the importance of the object- and semantic-level information in the prediction of visual attention. © 2014 ARVO.
Source Title: Journal of Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/82924
ISSN: 15347362
DOI: 10.1167/14.1.28
Appears in Collections:Staff Publications

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