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Proceedings Paper

Gaze-based visual feature extraction via DLPCCA for visual sentiment estimation
Author(s): Taiga Matsui; Naoki Saito; Takahiro Ogawa; Satoshi Asamizu; Miki Haseyama
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Paper 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.

Paper Details

Date Published: 22 March 2019
PDF: 5 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490Q (22 March 2019); doi: 10.1117/12.2516885
Show Author Affiliations
Taiga Matsui, Hokkaido Univ. (Japan)
Naoki Saito, Hokkaido Univ. (Japan)
Takahiro Ogawa, Hokkaido Univ. (Japan)
Satoshi Asamizu, National Institute of Technology, Kushiro College (Japan)
Miki Haseyama, Hokkaido Univ. (Japan)

Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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