International Conference on Enterprise Information Systems

Enterprise Information Systems pp 23-44 | Cite as

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

  • Marcos Vinicius Naves Bedo
  • 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)

Abstract

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.

Keywords

Fire detection Image descriptors Social media Extended-SQL 

Notes

Acknowledgements

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).

References

  1. 1.
    Russo, M.R.: Emergency management professional development: Linking information communication technology and social communication skills to enhance a sense of community and social justice in the 21st century. In: Crisis Management: Concepts, Methodologies, Tools and Applications, pp. 651–663. IGI Global (2013)Google Scholar
  2. 2.
    Kudyba, S.: Big Data, Mining, and Analytics: Components of Strategic Decision Making. Taylor & Francis Group, London (2014)CrossRefGoogle Scholar
  3. 3.
    Villela, K., Breiner, K., Nass, C., Mendonća, M., Vieira, V.: A smart and reliable crowdsourcing solution for emergency and crisis management. In: Interdisciplinary Information Management Talks, Podebrady, Czech Republic, pp. 213–220 (2014)Google Scholar
  4. 4.
    Sumathi, S., Esakkirajan, S.: Fundamentals of Relational Database Management Systems, vol. 47. Springer, Heidelberg (2007)MATHGoogle Scholar
  5. 5.
    Bedo, M.V.N., Blanco, G., Oliveira, W.D., Cazzolato, M., Costa, A.F., Rodrigues, J., Traina, A.J.M., Traina Jr., C.: Techniques for effective and efficient fire detection from social media images. In: International Conference on Enterprise Information Systems, Barcelona, Spain, pp. 34–46 (2015)Google Scholar
  6. 6.
    Chunyu, Y., Jun, F., Jinjun, W., Yongming, Z.: Video fire smoke detection using motion and color features. Fire Technol. 46, 651–663 (2010)CrossRefGoogle Scholar
  7. 7.
    Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18, 176–185 (2007)CrossRefGoogle Scholar
  8. 8.
    Ko, B.C., Cheong, K.H., Nam, J.Y.: Fire detection based on vision sensor and support vector machines. Fire Saf. J. 44, 322–329 (2009)CrossRefGoogle Scholar
  9. 9.
    Liu, C.B., Ahuja, N.: Vision based fire detection. In: International Conference on Pattern Recognition, vol. 4, pp. 134–137 (2004)Google Scholar
  10. 10.
    Tamura, S., Tamura, K., Kitakami, H., Hirahara, K.: Clustering-based burst-detection algorithm for web-image document stream on social media. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 703–708. IEEE (2012)Google Scholar
  11. 11.
    Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.: Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans. Circ. Syst. Video Technol. 25(2), 7–14 (2014)Google Scholar
  12. 12.
    Doeller, M., Kosch, H.: The mpeg-7 multimedia database system (mpeg-7 mmdb). J. Syst. Softw. 81, 1559–1580 (2008)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Aittola, M., Matinmikko, E.: Empirical evaluation of mpeg-7 xm color descriptors in content-based retrieval of semantic image categories. In: International Conference on Pattern Recognition, vol. 2, pp. 1021–1024 (2002)Google Scholar
  14. 14.
    Tjondronegoro, D., Chen, Y.P.: Content-based indexing and retrieval using mpeg-7 and x-query in video data management systems. World Wide Web 5, 207–227 (2002)CrossRefGoogle Scholar
  15. 15.
    Silva, Y.N., Aly, A.M., Aref, W.G., Larson, P.Å.: SimDB: a similarity-aware database system. In: ACM International Conference on Management of Data, Indianapolis, Indiana, USA, pp. 1243–1246 (2010)Google Scholar
  16. 16.
    Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag New York, Inc., Secaucus (2006)CrossRefMATHGoogle Scholar
  17. 17.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)Google Scholar
  18. 18.
    Barioni, M.C.N., Razente, H.L., Traina, A.J.M., Traina Jr., C.: Seamlessly integrating similarity queries in sql. Softw. Pract. Exp. 39, 355–384 (2009)Google Scholar
  19. 19.
    Silva, Y.N., Aref, W.G., Larson, P.Å., Pearson, S., Ali, M.H.: Similarity Queries: their conceptual evaluation, transformations, and processing. VLDB J. 22, 395–420 (2013)CrossRefGoogle Scholar
  20. 20.
    Kaster, D.S., Bugatti, P.H., Traina, A.J.M., Traina Jr., C.: FMI-SiR: a flexible and efficient module for similarity searching on Oracle database. J. Inf. Data Manage. 1, 229–244 (2010)Google Scholar
  21. 21.
    IEEE MultiMedia: Mpeg-7: the generic multimedia content description standard, part 1. IEEE MultiMedia 9, 78–87 (2002)Google Scholar
  22. 22.
    Sato, M., Gutu, D., Horita, Y.: A new image quality assessment model based on the MPEG-7 descriptor. In: Qiu, G., Lam, K.M., Kiya, H., Xue, X.-Y., Kuo, C.-C.J., Lew, M.S. (eds.) PCM 2010, Part I. LNCS, vol. 6297, pp. 159–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Kasutani, E., Yamada, A.: The mpeg-7 color layout descriptor: a compact image feature description for high-speed image/video segment retrieval. In: International Conference on Image Processing, vol. 1, pp. 674–677 (2001)Google Scholar
  24. 24.
    Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Circ. Syst. Video Technol. 11, 703–715 (2001)CrossRefGoogle Scholar
  25. 25.
    Sikora, T.: The mpeg-7 visual standard for content description-an overview. IEEE Circ. Syst. Video Technol. 11, 696–702 (2001)CrossRefGoogle Scholar
  26. 26.
    Park, D.K., Jeon, Y.S., Won, C.S.: Efficient use of local edge histogram descriptor. In: ACM Workshops on Multimedia, pp. 51–54. ACM (2000)Google Scholar
  27. 27.
    Wnukowicz, K., Skarbek, W.: Colour temperature estimation algorithm for digital images - properties and convergence. Opto Eletron. Rev. 11, 193–196 (2003)Google Scholar
  28. 28.
    Lee, K.L., Chen, L.H.: An efficient computation method for the texture browsing descriptor of mpeg-7. Image Vis. Comput. 23, 479–489 (2005)CrossRefGoogle Scholar
  29. 29.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search - The Metric Space Approach. Advances in Database Systems, vol. 32. Springer, Berlin, Heidelberg (2006)MATHGoogle Scholar
  30. 30.
    Bedo, M.V.N., Traina, A.J.M., Traina Jr., C.: Seamless integration of distance functions and feature vectors for similarity-queries processing. J. Inf. Data Manage. 5, 308–320 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcos Vinicius Naves Bedo
    • 1
  • 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

Personalised recommendations