@inproceedings{zaharieva_social_2015, address = {Shanghai, China}, title = {Social {Event} {Mining} in {Large} {Photo} {Collections}}, 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.}, booktitle = {Proceedings of the {International} {Conference} on {Multimedia} {Retrieval}}, publisher = {ACM Press}, author = {Zaharieva, Maia and Zeppelzauer, Matthias and Del Fabro, Manfred and Schopfhauser, Daniel}, month = mar, year = {2015}, keywords = {2015, Center for Artificial Intelligence, Computer Vision, Creative Industries, Department Technologie, FH SP Data Analytics \& Visual Computing, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Machine Learning, Media Computing Group, Pattern recognition, Publikationstyp Schriftpublikation, Wiss. Beitrag, best, best-mzeppelzauer, peer-reviewed, visual computing}, }