Tapestry场景的社会传感器组合
Social-Sensor Composition for Tapestry Scenes
摘要：
社交媒体平台的广泛使用和大量图像数据为感知、收集和分享事件信息创造了独特的机会。其潜在应用之一是利用众包的社交媒体图像来创建挂毯场景，用于指定位置和时间间隔的景物分析。然而，现有的尝试忽略了时间语义相关性和时空演变的图像和面向方向的场景重建。我们提出了一种新的社会传感器云（SocSen）服务组合方法，形成挂毯场景的景物分析。新颖性在于利用图像和图像元信息来绕过昂贵的传统图像处理技术来重建场景。元数据，如地理位置，时间和图像的视角被建模为SocSen服务的非功能属性。我们的主要贡献在于提出了一个上下文和方向感知的时空聚类和推荐方法，用于选择一组时间和语义相似的服务，以组成最好的可用SocSen服务。基于真实数据集的分析结果证明了所提出的方法的性能。
英文摘要：
The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time and angle of view of an image are modelled as non-functional attributes of a SocSen service. Our major contribution lies on proposing a context and direction-aware spatio-temporal clustering and recommendation approach for selecting a set of temporally and semantically similar services to compose the best available SocSen services. Analytical results based on real datasets are presented to demonstrate the performance of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
