Semantic event detection for consumer photo and video collections

The automatic detection of semantic events in userspsila image and video collections is an important technique for content management and retrieval. In this paper we propose a novel semantic event detection approach by considering an event-level bag-of-features (BOF) representation to model typical events. Based on this BOF representation, semantic events are detected in a concept space instead of the original low-level visual feature space. There are two advantages of our approach: we can avoid the sensitivity problem by decreasing the influence of difficult or erroneous images or videos in measuring the event-level similarity; also we can utilize the power of higher-level concept scores in describing semantic events. Experiments over a large real consumer database confirm the effectiveness of our approach.