Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction

TL;DR

This paper introduces a heuristics-based model that uses social media images and machine learning to select and compose relevant images for reconstructing scenes as mosaics, demonstrating promising initial results.

Contribution

It presents a novel heuristics and machine learning-driven approach for social-sensor service selection and scene reconstruction through image mosaics.

Findings

Feasibility of the proposed mosaic composition model.

Effective filtering of non-relevant social media images.

Initial analytical results support the approach's potential.

Abstract

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.