Aesthetic image captioning on the FAE-Captions dataset. (July 2022)

• Record Type: Journal Article
• Authors: Jin, Xin; Lv, Jianwen; Zhou, Xinghui; Xiao, Chaoen; Li, Xiaodong; Zhao, Shu
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Aesthetic assessment -- Image captioning -- Weakly-supervised learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107866
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: At present, most of the research on image aesthetics focuses on scoring pictures. We propose Aesthetic Assessment of Images, which means the dense aesthetic captioning. The image captioning model uses many photos for www.flickr.com as a training set, including lots of captions about every photo meantime. Then we use LDA for aesthetic topics and active learning to screen sentence. The remaining sentence dataset, we called FAE-Captions, is a professional dataset on the topic of image aesthetics. Put the dataset into a CNN-LSTM model for predicting and generating answers. In order to let neural network connected with aesthetics, we propose an aesthetic loss function for many aesthetics topics additionally.