Graph-Based Early-Fusion for Flood Detection

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), ther...

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Bibliographic Details
Published in2018 25th IEEE International Conference on Image Processing (ICIP) pp. 1048 - 1052
Main Authors De O. Werneck, Rafael, Dourado, Icaro C., Fadel, Samuel G., Tabbone, Salvatore, Da S. Torres, Ricardo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
early fusion
Feature extraction
flood detection
graph-based fusion
image representation
MediaEval
Metadata
Monitoring
Principal component analysis
Social network services
Streaming media
Visualization
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451011