Graph-Based Early-Fusion for Flood Detection

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


  • Rafael O. De Werneck
  • Icaro C. Dourado
  • Samuel G. Fadel
  • Salvatore Tabbone
  • Ricardo Da S. Torres

Flooding is one of the most harmful natural disasters, as it poses danger to both buildings and human lives. Therefore, it is fundamental to monitor these disasters to define prevention strategies and help authorities in damage control. With the wide use of portable devices (e.g., smartphones), there is an increase of the documentation and communication of flood events in social media. However, the use of these data in monitoring systems is not straightforward and depends on the creation of effective recognition strategies. In this paper, we propose a fusion-based recognition system for detecting flooding events in images extracted from social media. We propose two new graph-based early-fusion methods, which consider multiple descriptions and modalities to generate an effective image representation. Our results demonstrate that the proposed methods yield better results than a traditional early-fusion method and a specialized deep neural network fusion solution.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Number of pages5
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date29.08.2018
Article number8451011
ISBN (Print)978-1-4799-7062-9
ISBN (Electronic)9781479970612
Publication statusPublished - 29.08.2018
Externally publishedYes
Event25th IEEE International Conference on Image Processing - ICIP 2018 - Athens, Greece
Duration: 07.10.201810.10.2018
Conference number: 25

    Research areas

  • Early fusion, Flood detection, Graph-based fusion, Image representation, MediaEval