# Social-Sensor Composition for Tapestry Scenes

arXiv: https://arxiv.org/abs/2003.13684 · HTML: https://mlsanity.com/paper/2003.13684/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Privacy-Aware Identity Cloning Detection based on Deep Forest](https://mlsanity.com/paper/2110.10897/) (2021)
- [Cloned Identity Detection in Social-Sensor Clouds based on Incomplete Profiles](https://mlsanity.com/paper/2411.01329/) (2024)
- [Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services](https://mlsanity.com/paper/2403.12045/) (2023)
- [Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication](https://mlsanity.com/paper/2508.09665/) (2025)

## Abstract (arXiv)

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.