10.1184/R1/6473678.v1 Yipei Wang Yipei Wang Shourabh Rawat Shourabh Rawat Florian Metze Florian Metze Semi-automatic audio semantic concept discovery for multimedia retrieval Carnegie Mellon University 2014 audio semantic concept discovery semiautomatic multimedia retrieval 2014-05-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Semi-automatic_audio_semantic_concept_discovery_for_multimedia_retrieval/6473678 <p>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.</p>