Social-Sensor Composition for Tapestry Scenes
This work addresses the challenge of efficiently reconstructing scenes from social media data for applications in event sensing and analysis, though it appears incremental as it builds on existing social-sensor concepts with specific enhancements.
The paper tackles the problem of creating tapestry scenes from crowdsourced social media images for scene analysis by addressing the neglect of temporal-semantic relevance and spatio-temporal evolution in existing methods, resulting in a proposed approach that uses metadata to bypass expensive image processing and demonstrates performance with analytical results on real datasets.
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.