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World Wide Web

, Volume 22, Issue 5, pp 1893–1911 | Cite as

Multimodal deep learning based on multiple correspondence analysis for disaster management

  • Samira PouyanfarEmail author
  • Yudong Tao
  • Haiman Tian
  • Shu-Ching Chen
  • Mei-Ling Shyu
Article
Part of the following topical collections:
  1. Special Issue on Big Data for Effective Disaster Management

Abstract

The fast and explosive growth of digital data in social media and World Wide Web has led to numerous opportunities and research activities in multimedia big data. Among them, disaster management applications have attracted a lot of attention in recent years due to its impacts on society and government. This study targets content analysis and mining for disaster management. Specifically, a multimedia big data framework based on the advanced deep learning techniques is proposed. First, a video dataset of natural disasters is collected from YouTube. Then, two separate deep networks including a temporal audio model and a spatio-temporal visual model are presented to analyze the audio-visual modalities in video clips effectively. Thereafter, the results of both models are integrated using the proposed fusion model based on the Multiple Correspondence Analysis (MCA) algorithm which considers the correlations between data modalities and final classes. The proposed multimodal framework is evaluated on the collected disaster dataset and compared with several state-of-the-art single modality and fusion techniques. The results demonstrate the effectiveness of both visual model and fusion model compared to the baseline approaches. Specifically, the accuracy of the final multi-class classification using the proposed MCA-based fusion reaches to 73% on this challenging dataset.

Keywords

Multimodal deep learning Multiple Correspondence Analysis (MCA) Disaster information management 

Notes

Acknowledgments

This research is partially supported by NSF CNS-1461926.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of MiamiCoral GablesUSA

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