Paper Info
Title
Understanding Infographics through Textual and Visual Tag Prediction.
Abstract
We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as input, our computational approach automatically outputs textual and visual elements predicted to be representative of the infographic content. Concretely, from a curated dataset of 29K large infographic images sampled across 26 categories and 391 tags, we present an automated two step approach. First, we extract the text from an infographic and use it to predict text tags indicative of the infographic content. And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i.e. visual hashtags. We report performances on a categorization and multi-label tag prediction problem and compare our proposed visual hashtags to human annotations.
Year
Venue
Field
2017
arXiv: Computer Vision and Pattern Recognition
Categorization,Information retrieval,Pattern recognition,Computer science,Infographic,Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1709.09215
2
PageRank 
References 
Authors
0.35
6
8
Name
Order
Citations
PageRank
Zoya Gavrilov128716.20
Sami Alsheikh220.35
Spandan Madan3282.05
Adrià Recasens4746.55
Kimberli Zhong520.35
Hanspeter Pfister65933340.59
Frédo Durand78625414.94
Aude Oliva85121298.19