Event Detection and Scene Attraction by Very Simple Contextual Cues

We detect and arrange events in private photo archives by putting these photos into context. The problem is seen as a fully automated mining in one's personal life and behavior without actually recognizing the content of the photos. To this end, we build a contextual meaningful hierarchy of events. With the analysis of very simple cues of time, space and perceptual visual appearance we are refining and validating the event borders and their relation in an iterative way. Beginning with discriminating between routine and unusual events, we are able to robustly recognize the basic nature of an event. Further combination of the given cues efficiently gives a hierarchy of events that coincides with the given ground-truth at an F-measure of 0.83 for event detection and 0.70 for its hierarchical representation. We process the given task in a fully unsupervised and computationally inexpensive manner. Using standard clustering and machine learning techniques, sparse events in the collection wou...