NDLI: Social Event Mining in Large Photo Collections

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Social Event Mining in Large Photo Collections

Content Provider	ACM Digital Library
Author	Del Fabro, Manfred Zeppelzauer, Matthias Zaharieva, Maia Schopfhauser, Daniel
Abstract	A significant part of publicly available photos on the Internet depicts a variety of different social events. In order to organize this steadily growing media content and to make it easily accessible, novel indexing methods are required. Essential research questions in this context concern the efficient detection (clustering), classification, and retrieval of social events in large media collections. In this paper we explore two aspects of social events mining. First, the initial clustering of a given photo collection into single events and, second, the retrieval of relevant social events based on user queries. For both aspects we employ commonly available metadata information, such as user, time, GPS data, and user-generated textual descriptions. Performed evaluations in the context of social event detection demonstrate the strong generalization ability of our approach and the potential of contextual data such as time, user, and location. Experiments with social event retrieval clearly indicate the open challenge of mapping between previously detected event clusters and heterogeneous user queries.
Starting Page	11
Ending Page	18
Page Count	8
File Format	PDF
ISBN	9781450332743
DOI	10.1145/2671188.2749313
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Flickr Event detection Event clustering Event retrieval Mediaeval benchmark
Content Type	Text
Resource Type	Article

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