Journal
JOURNAL OF VISION
Volume 14, Issue 1, Pages -Publisher
ASSOC RESEARCH VISION OPHTHALMOLOGY INC
DOI: 10.1167/14.1.28
Keywords
visual saliency; saliency attribute; object saliency; semantic saliency; dataset; computational model
Categories
Funding
- Singapore NRF under IRC@SG Funding Initiative
- Singapore Ministry of Education Academic Research Fund Tier 1 [R-263-000-648-133]
Ask authors/readers for more resources
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 semantic-level 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available