Feature-centric ranking algorithms for georeferenced video search

Abstract

While it is commonplace to retrieve photos showing a particular feature (e.g. through tools such as Google Pictures or Bing Images), spatial approaches for retrieving videos showing a particular feature (e.g. a building) have yet to be established. is article proposes ve ranking algorithms to query georeferenced videos for a speci c feature based on the videos' spatio-temporal metadata. 12 relevance criteria for feature-centric video ranking were compiled from a focus group discussion. From these, four criteria have been selected for implementation: "Feature Depiction", "Feature Illumination", "Feature Visibility Duration", and "Distance to Feature". ese criteria were implemented in ve algorithms and evaluated regarding e ciency and user perceived plausibility. e evaluation suggests that the "Feature Visibility Duration" of the video's viewshed with the queried feature geometry o ers a good trade-o between computationally performant and cognitive plausible ranking. e obtained results are relevant to user-centered approaches for interacting with georeferenced videos. CCS CONCEPTS •Information systems →Information retrieval; •Human-centered computing →Human computer interaction (HCI);

Spatial search strategies for open government data

“…Relevance ranking has been examined for georeferenced videos in [1,19]. Ay et al [1] proposed algorithms for ranking of videos based on their spatial and temporal properties, and Fritze et al [19] reported on 12 criteria suggested by users to determine the relevance of georeferenced videos. Despite these works, and work examining geographic relevance (e.g.…”