@inproceedings{1fd446489b69409498aead13e35ed8dc,
title = "Predicting Image Emotion Distribution by Emotional Region",
abstract = "Recent studies on image emotion prediction mainly focus on classifying images into a certain emotion category, but single label cannot reflect peoples' multiple emotion to image. To get more realistic results, we study image emotion distribution problem. In the most image emotion tasks, features are extracted from the whole image, but not each part makes contribution to emotion, so features from the whole image contain noises. In order to get discriminative features, we propose to leverage the heatmap generated by Fully Convolutional Networks (FCN) to select the Region of Interest (ROI) from an image which represents the image emotion most. Both high-level features and hand-crafted features from ROI are fused to train Support Vector Regressors (SVRs) to predict emotion distribution. Extensive experiments conducted on two widely used datasets demonstrate that emotional region is selected out through our method so that emotion distribution prediction performances are improved.",
keywords = "FCN, Image emotion distribution, SVR, emotional region",
author = "Yangyu Fan and Hansen Yang and Zuhe Li and Shu Liu",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 ; Conference date: 13-10-2018 Through 15-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2018.8633190",
language = "英语",
series = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Li and Qingli Li and Lipo Wang",
booktitle = "Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018",
}