Name
Abstract | ||
|---|---|---|
In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as \"socially dominant, \" \"energetic, \" and \"trustworthy, \" and propose a hierarchical model that builds on the layer of syntactical attributes, such as \"smile\" and \"waving hand, \" to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1, 124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.35 | CVPR |
Keywords | Field | DocType |
politics,politicians,political communication,image representation,social sciences,image intent prediction,trustworthy,mass media,syntactical image representation,syntactical attributes,psychology,communicative intents,socially dominant images,visual sentiment analysis,computer vision,behavioral studies,media application,visual persuasion,energetic images,media analysis,persuasive intent latent,visual persuasion, communicative intents, human attributes, visual sentiment analysis, media application,human attributes,correlation,face,media,visualization | Computer vision,Persuasion,Political communication,Trustworthiness,Computer science,New class,Mass media,Artificial intelligence,Parsing,Hierarchical database model,Politics | Conference |
ISSN | Citations | PageRank |
1063-6919 | 24 | 1.67 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungseock Joo | 1 | 52 | 4.61 |
| Weixin Li | 2 | 28 | 2.75 |
| Francis F. Steen | 3 | 24 | 1.67 |
| Song-Chun Zhu | 4 | 6580 | 741.75 |