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.

中文翻译：

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用于场景重建的基于启发式的社会传感器服务马赛克

我们提出了一种基于启发式的社会传感器云服务选择和合成模型来重建马赛克场景。所提出的方法利用众包社交媒体图像来创建图像马赛克，以在指定位置和时间间隔重建场景。这种新颖的方法依赖于基于图像元数据定义的一组特征来确定服务的相关性和可组合性。开发了新颖的启发式方法来过滤掉不相关的服务。多种机器学习策略被用来产生平滑的服务组合，从而产生由地理位置和时间索引的相关图像的马赛克。初步分析结果证明了所提出的组成模型的可行性。