Semi-automatic audio semantic concept discovery for multimedia retrieval
Huge amount of videos on the Internet have rare textual information, which makes video retrieval challenging given a text query. Previous work explored semantic concepts for content analysis to assist retrieval. However, the human-defined concepts might fail to cover the data and there is a potential gap between these concepts and the semantics expected from user's query. Also, building a corpus is expensive and time-consuming. To address these issues, we propose a semi-automatic framework to discover the semantic concepts. We limit ourselves in audio modality here. In the paper, we also discuss how to select meaningful vocabulary from the discovered hierarchical sub-categories and provide an approach to detect all the concepts without further annotation. We evaluate the method on NIST 2011 multimedia event detection (MED) dataset.