DocumentCode :
3724357
Title :
Extracting Topic-Related Photos in Density-Based Spatiotemporal Analysis System for Enhancing Situation Awareness
Author :
Tatsuhiro Sakai;Keiichi Tamura;Hajime Kitakami
Author_Institution :
Grad. Sch. of Inf. Sci., Hiroshima City Univ., Hiroshima, Japan
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
201
Lastpage :
206
Abstract :
Recently, people have begun to diligently post their situation, particularly during a crisis, on social media, therefore, the enhancement of situation awareness using social data is one of the most attractive research subjects. In this paper, we propose a novel density-based spatiotemporal system with a photo image classifier. The photo image classifier, which allows the system to enhance situation awareness during a crisis by showing accurate topic-related photos, is integrated using a support vector machine (SVM) based on the Bag-of-Features (BoF) model into the conventional density-based spatiotemporal system. To evaluate the proposed system, we used an actual data set related to a weather topic, "rain," in Japan. The experimental results indicate that the proposed system can extract photo images related to the weather topic "rain" with high accuracy and recall levels.
Keywords :
"Spatiotemporal phenomena","Media","Support vector machines","Clustering algorithms","Meteorology","Earthquakes","Training data"
Publisher :
ieee
Conference_Titel :
Advanced Applied Informatics (IIAI-AAI), 2015 IIAI 4th International Congress on
Print_ISBN :
978-1-4799-9957-6
Type :
conf
DOI :
10.1109/IIAI-AAI.2015.240
Filename :
7373901
Link To Document :
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