Density-based Spatiotemporal Analysis System with Photo Image Classifier using the BoF Model

Tatsuhiro Sakai, Keiichi Tamura, Hajime Kitakami


Recently, people have begun to diligently post situational updates, particularly during natural disasters, such as an earthquake, typhoon, heavy storm, and snowfall, on social media; therefore, the enhancement of situation awareness in the real world using social data is one of the most attractive research subjects. In our previous work, we developed a density-based spatiotemporal system to identify topic-related areas in which there are a huge number of geo-tagged tweets related to a topic are posted. In this paper, we propose a novel densitybased spatiotemporal system with a photo image classifier in order to enhance situation awareness by showing accurate topic-related photos. The photo image classifier using a support vector machine (SVM) based on the Bag-of-Features (BoF) model is integrated into the conventional density-based spatiotemporal system. To evaluate the proposed system, we used actual data sets related to weather topics, “heavy rain” and “heavy snow,” in Japan. The experimental results indicate that the proposed system can extract photo images related to these weather topics with high accuracy and recall levels.

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