Discovering Collective Narratives of Theme Parks from Large Collections of Visitors' Photo Streams

Gunhee Kim, Leonid Sigal
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
We present an approach for generating pictorial storylines from large collections of online photo streams shared by visitors to theme parks (e.g. Disneyland), along with publicly available information such as visitor's maps. The story graph visualizes various events and activities recurring across visitors' photo sets, in the form of hierarchically branching narrative structure associated with attractions and districts in theme parks. We first estimate story elements of each photo stream,
more » ... ing the detection of faces and supporting objects, and attraction-based localization. We then create spatio-temporal story graphs via an inference of sparse time-varying directed graphs. Through quantitative evaluation and crowdsourcing-based user studies via Amazon Mechanical Turk, we show that the story graphs serve as a more convenient mid-level data structure to perform photobased recommendation tasks than other alternatives. We also present storybook-like demo examples regarding exploration, recommendation, and temporal analysis, which may be most beneficial uses of the story graphs to visitors.
doi:10.1145/2783258.2788569 dblp:conf/kdd/KimS15 fatcat:xhztwmtaajckjp62jpqjerswae