Predicting celebrity attendees at public events using stock photo metadata

The large collections of news images available from stock photo agencies provide interesting insights into how different celebrities are related to each other, in terms of the events they attend together and also in terms of how often they are photographed together. In this paper, we leverage such collections to predict which celebrities will attend future events. The main motivation for this is in the event-based indexing of online collections of multimedia content, an area that has attracted much attention in recent years. Based on the metadata associated with a corpus of stock photos, we propose a language model for predicting celebrities attending future events. A temporal hierarchical version of the language model exploits fresh data while still making use of all historical data. We extract a social network from co-appearance of public figures in the events depicted in the photographs, and combine this latent social information with the language model to further improve prediction accuracy. The experimental results show that combining textual, network and temporal information gives the best prediction performance. Our analysis also shows that the prediction models, when trained by the most recent data, are most accurate for political and sports events.