Exploitation of time constraints for (sub-)event recognition

The aim of this paper is threefold: (a) to introduce a dataset for the recognition of events and sub-events in photographs taken by common users; (b) to propose event-based classification to achieve a more accurate labeling of event-related photo collections; (c) to use time clustering information to improve the sub-event recognition in an efficient Bag of Features classification approach. The dataset is organized according to event models and provides a collection of sample instances that allow the comparison of different recognition systems. On this basis, we will demonstrate how the use of time clustering together with multiple image visual features can outperform single image classification.