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.

With users increasingly using their mobile devices such as smartphones as digital photo albums, effective methods for managing these collections are becoming increasingly important. Standard solutions provide only limited facilities for organising, browsing and searching image collections on mobile devices, making it challenging and time-consuming to locate images of interest.

We present a video browsing tool that combines advantages of the hierarchical browsing concept with 3D projection and multi-threaded programming in order to provide a convenient and efficient interface. The tool allows for instantaneous hierarchical browsing of video and uses a dynamic approach (i.e. tree of playable video segments instead of static key frames)that also supports parallel playback.