Fire Detection from Social Media Images by Means of Instance-Based Learning

  • Marcos Vinicius Naves BedoEmail author
  • William Dener de Oliveira
  • Mirela Teixeira Cazzolato
  • Alceu Ferraz Costa
  • Gustavo Blanco
  • Jose F. RodriguesJr.
  • Agma J. M. Traina
  • Caetano TrainaJr.
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 241)


Social media can provide valuable information to support decision making in crisis management, such as in accidents, explosions, and fires. However, much of the data from social media are images, which are uploaded at a rate that makes it impossible for human beings to analyze them. To cope with that problem, we design and implement a database-driven architecture for fast and accurate fire detection named FFireDt. The design of FFireDt uses the instance-based learning through indexed similarity queries expressed as an extension of the relational Structured Query Language. Our contributions are: (i) the design of the Fast-Fire Detection (\(FFireDt\)), which achieves efficiency and efficacy rates that rival to the state-of-the-art techniques; (ii) the sound evaluation of 36 image descriptors, for the task of image classification in social media; (iii) the evaluation of content-based indexing with respect to the construction of instance-based classification systems; and (iv) the curation of a ground-truth annotated dataset of fire images from social media. Using real data from Flickr, the experiments showed that system \(FFireDt\) was able to achieve a precision for fire detection comparable to that of human annotators. Our results are promising for the engineering of systems to monitor images uploaded to social media services.


Fire detection Image descriptors Social media Extended-SQL 



This research is supported, in part, by FAPESP, CNPq, CAPES, STIC-AmSud, the RESCUER project, funded by the European Commission (Grant: 614154) and by the CNPq/MCTI (Grant: 490084/2013-3).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcos Vinicius Naves Bedo
    • 1
    Email author
  • William Dener de Oliveira
    • 1
  • Mirela Teixeira Cazzolato
    • 1
  • Alceu Ferraz Costa
    • 1
  • Gustavo Blanco
    • 1
  • Jose F. RodriguesJr.
    • 1
  • Agma J. M. Traina
    • 1
  • Caetano TrainaJr.
    • 1
  1. 1.Institute of Mathematics and Computer ScienceUniversity of São PauloSão Carlos/SPBrazil

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