Extracting Topic-Related Photos in Density-Based Spatiotemporal Analysis System for Enhancing Situation Awareness
Extracting Topic-Related Photos in Density-Based Spatiotemporal Analysis System for Enhancing Situation Awareness
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DOI：
10.1109/iiai-aai.2015.240
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Tatsuhiro Sakai;Keiichi Tamura;H. Kitakami
中科院分区：
文献类型：
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作者：
Tatsuhiro Sakai;Keiichi Tamura;H. Kitakami
最近，人们开始在社交媒体上勤奋地发布自己的情况，特别是在危机期间，因此，利用社会数据增强态势意识是最具吸引力的研究课题之一。在本文中，我们提出了一种新的基于密度的具有照片图像分类器的时空系统。照片图像分类器允许系统通过显示准确的主题相关照片来增强危机期间的情况感知，该分类器使用基于特征袋(BoF)模型的支持向量机(SVM)集成到传统的基于密度的时空系统中。为了评估所提出的系统，我们使用了一个与日本的天气主题“雨”相关的实际数据集。实验结果表明，该系统能够以较高的准确率和召回率提取出与天气主题“雨”相关的图片图像。
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.