Information Block Detection in Infographic Based on Spatial Proximity and Structural Similarity (Student Abstract)

Authors

  • Jie Lin School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Xin Wu School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Jianwei Lu School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
  • Yi Cai School of Software Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education

Keywords:

Infographic Understanding, Information Block Detection, Gestalt Organization Principle

Abstract

The infographic is a type of visualization chart used to display information. Existing infographic understanding works utilize spatial proximity to group elements into information blocks. However, these works ignore structural features such as background color and boundary, which results in poor performance towards complex infographic. We propose Spatial and Structural Feature Extraction model to group elements based on spatial proximity and structural similarity. We introduce a new dataset towards information block detection. Experiments show that our model can effectively identify the information blocks in the infographic.

Downloads

Published

2021-05-18

How to Cite

Lin, J., Wu, X., Lu, J., & Cai, Y. (2021). Information Block Detection in Infographic Based on Spatial Proximity and Structural Similarity (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15829-15830. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17911

Issue

Section

AAAI Student Abstract and Poster Program