Can we understand van gogh's mood?: learning to infer affects from images in social networks

Authors:
Jia Jia;Sen Wu;Xiaohui Wang;Peiyun Hu;Lianhong Cai;Jie Tang
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 20th ACM international conference on Multimedia
Year:
2012

Citing 3
Cited 3

Affective prediction in photographic images using probabilistic affective model

Proceedings of the ACM International Conference on Image and Video Retrieval
Affective image classification using features inspired by psychology and art theory

Proceedings of the international conference on Multimedia
Learning to infer social ties in large networks

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III

Sentribute: image sentiment analysis from a mid-level perspective

Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content

Proceedings of the 21st ACM international conference on Multimedia
Large-scale visual sentiment ontology and detectors using adjective noun pairs

Proceedings of the 21st ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Can we understand van Gogh's mood from his artworks? For many years, people have tried to capture van Gogh's affects from his artworks so as to understand the essential meaning behind the images and catch on why van Gogh created these works. In this paper, we study the problem of inferring affects from images in social networks. In particular, we aim to answer: What are the fundamental features that reflect the affects of the authors in images? How the social network information can be leveraged to help detect these affects? We propose a semi-supervised framework to formulate the problem into a factor graph model. Experiments on 20,000 random-download Flickr images show that our method can achieve a precision of 49% with a recall of 24% on inferring authors'affects into 16 categories. Finally, we demonstrate the effectiveness of the proposed method on automatically understanding van Gogh's Mood from his artworks, and inferring the trend of public affects around special event.