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Extracting Topic-related Photos in Density-based Spatiotemporal Analysis System for Enhancing Situation Awareness

机译:提取基于密度的时空分析系统中的主题相关照片,提升局势意识

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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.
机译:最近,人们已经开始努力地发布他们的情况,特别是在社交媒体上的危机期间;因此,利用社会数据提高了情况意识是最具吸引力的研究科目之一。本文提出了一种具有光图像分类器的新型密度的时空系统。通过显示基于特征袋(BOF)模型,将系统通过显示精确的主题相关的照片在危机期间增强危机中的情况感知的危机中的情境感知基于时空系统。为了评估所提出的系统,我们使用与日本的天气主题有关的实际数据集,“雨”。实验结果表明,所提出的系统可以提取与天气主题“雨”相关的照片图像,具有高精度和召回水平。

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