Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation

Taiga Matsui; Naoki Saito; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama

doi:10.1117/12.2516885

22 March 2019 Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation

Taiga Matsui, Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110490Q (2019) https://doi.org/10.1117/12.2516885
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

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.

Citation Download Citation

Taiga Matsui, Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, and Miki Haseyama "Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490Q (22 March 2019); https://doi.org/10.1117/12.2516885

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