Visual Persuasion: Inferring Communicative Intents of Images
Jungseock Joo, Weixin Li, Francis F. Steen, and Song-Chun Zhu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. [pdf]
- Is a Picture Worth 1,000 Polls? by Virginia Postrel. Bloomberg View. June 25, 2014.
In this paper we introduce the novel problem of understanding
visual persuasion. Modern mass media and advertising
make extensive use of images and video to present
arguments and influence public opinion, and their techniques
are widely studied in media research, political science,
and psychology, typically using small, hand-coded
datasets. We propose to extend the significant advances
in syntactic analyses, such as the detection and identification
of objects and sentiments in images and video, to
the higher-level challenge of understanding the underlying
communicative intent implied in the images. We define the
problem of inferring communicative intents from images in
a computational framework, and demonstrate the feasibility
of progress in a case study from politics, a domain of intense
competitive persuasion with continuously measurable
outcomes in opinion polls. The quantitative results demonstrate
that a systematic focus on visual persuasion opens up
the field of computer vision to a new class of investigations
around mediated images, intersecting with media analysis,
psychology, and political communication.
Dataset - Persuasive Portraits of Politicians
Our dataset contains 1,124 photographs of 8 US politicians. Each image was labeled with 9 intent dimensions as well as 15 syntactical attributes.
The annotation can be freely used for academic purpose.
Please contact Jungseock for the full access.