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.

中文翻译：

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挂毯场景的社交传感器组合

社交媒体平台的广泛使用和海量图像数据为感知、收集和共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社交媒体图像创建挂毯场景，用于指定位置和时间间隔的场景分析。然而，现有的尝试忽略了图像的时间语义相关性和时空演化以及面向方向的场景重建。我们提出了一种新颖的社交传感器云 (SocSen) 服务组合方法，以形成用于场景分析的挂毯场景。新颖之处在于利用图像和图像元信息绕过昂贵的传统图像处理技术来重建场景。元数据，例如地理位置，图像的时间和视角被建模为 SocSen 服务的非功能属性。我们的主要贡献在于提出了一种上下文和方向感知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来组成最佳可用的 SocSen 服务。提出了基于真实数据集的分析结果，以证明所提出方法的性能。