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

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

Content Provider	ACM Digital Library
Author	Sigal, Leonid Kim, Gunhee
Abstract	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, including 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 photo-based 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.
Starting Page	1899
Ending Page	1908
Page Count	10
File Format	PDF MP4
ISBN	9781450336642
DOI	10.1145/2783258.2788569
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Summarization and exploration of multimedia data User modeling Photo storylines
Content Type	Audio Text
Resource Type	Article

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