Title
Fire Detection from Social Media Images by Means of Instance-Based Learning.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-29133-8_2
Lecture Notes in Business Information Processing
Keywords
Field
DocType
Fire detection,Image descriptors,Social media,Extended-SQL
SQL,Data mining,Architecture,Instance-based learning,Social media,Computer science,Upload,Search engine indexing,Artificial intelligence,Contextual image classification,Fire detection,Machine learning
Conference
Volume
ISSN
Citations 
241
1865-1348
1
PageRank 
References 
Authors
0.40
18
8