Interest Estimation for Images Using Eye Gaze-based Visual and Text Features via DLPCCA
Masanao Matsumoto, Naoki Saito, Takahiro Ogawa, Miki Haseyama · 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech) · 2020
This paper presents a new method to realize estimation performance improvement of user-specific interests for images. The proposed method computes projections which transform visual and text features to eye gaze-based features that reflect user's interests by utilizing discriminative locality preserving canonical correlation analysis (DLPCCA). DLPCCA can calculate projections suitable for interest estimation by considering the class information and locality of data structure. Experimental results are shown for verifying the effectiveness of our method.