Estimation of Visual Attention via Canonical Correlation between Visual and Gaze-based Features
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) · 2019
This paper presents a method for estimating visual attention via canonical correlation between visual and gaze-based features. The proposed method estimates user-specific visual attention by comparing a test image with training images including their corresponding individual eye gaze data in a common space. Specifically, canonical correlation analysis can derive projections which enable comparison between visual and gaze-based features in the common space. Therefore, given the new test image, our method projects its visual features to the common space and can estimate visual attention. Experimental results show the effectiveness of the proposed method.