Interest Estimation for Images Based on Eye Gaze-based Visual and Text Features
Masanao Matsumoto, Naoki Saito, Takahiro Ogawa, Miki Haseyama · 2019
This paper presents an estimation method of user-specific interests for images. The proposed method computes a projection which maximizes the correlation between “eye gaze data which are collected while watching images” and “visual and text features” by utilizing Canonical Correlation Analysis (CCA). Since eye gaze data reflect user's interests, new visual and text features calculated by using obtained projections can be also expected to reflect user's interests. Then accurate estimation of user-specific interests for images via Support Vector Machine (SVM) becomes feasible from these features. Experimental results show the effectiveness of our method.