MvLFDA-based video preference estimation using complementary properties of features
Akira Toyoda, Takahiro Ogawa, Miki Haseyama · 2017
This paper presents a new method to estimate users' video preferences using complementary properties of features via Multiview Local Fisher Discriminant Analysis (MvLFDA). The proposed method first extracts multiple visual features from video frames and electroencephalogram (EEG) features from users' EEG signals recorded during watching video. Then we calculate EEG-based visual features by applying Locality Preserving Canonical Correlation Analysis (LPCCA) to each visual feature and EEG features. The EEG-based visual features reflect users' preferences since the correlation between visual features and EEG features which reflect users' preferences is maximized. Next, MvLFDA, which is newly derived in this paper, integrates multiple EEG-based visual features. Since MvLFDA explores complementary properties of different features, it can be expected that the features obtained by integrating multiple EEG-based visual features are more effective for users' preference estimation than each EEG-based visual feature. The biggest contribution of this paper is the new derivation of MvLFDA. Then successful estimation of users' video preferences becomes feasible using features obtained by MvLFDA.