Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation
Taiga Matsui, Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama · International Workshop on Advanced Image Technology (IWAIT) 2019 · 2019
This paper presents gaze-based visual feature extraction via Discriminative Locality Preserving Canonical Correlation Analysis (DLPCCA) for visual sentiment estimation. The proposed method calculates novel visual features reflecting users’ visual sentiment by applying DLPCCA to gaze and original visual features. Consequently, accurate visual sentiment estimation becomes feasible by utilizing the novel visual features derived by the proposed method.