Authors:
Marcos V. N. Bedo
;
Gustavo Blanco
;
Willian D. Oliveira
;
Mirela T. Cazzolato
;
Alceu F. Costa
;
Jose F. Rodrigues Jr.
;
Agma J. M. Traina
and
Caetano Traina Jr.
Affiliation:
University of São Paulo, Brazil
Keyword(s):
Fire Detection, Feature Extraction, Evaluation Functions, Image Descriptors, Social Media.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Human-Computer Interaction
;
Multimedia Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Crowdsourcing and social media could 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 in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFireDt), which combines feature extractor and evaluation functions to support instance-based learning; (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences – the Flickr-Fire dataset; and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFireDt
was able to achieve a precision for fire detection that was comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media and crowdsourcing.
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