Distribution-based concept selection for concept-based video retrieval

Query-to-concept mapping plays one of the keys to concept-based video retrieval. Conventional approaches try to find concepts that are likely to co-occur in the relevant shots from the lexical or statistical aspects. However, the high probability of co-occurrence alone cannot ensure its effectiveness to distinguish the relevant shots from the irrelevant ones. In this paper, we propose distribution-based concept selection (DBCS) for query-to-concept mapping by analyzing concept score distributions of within and between relevant and irrelevant sets. In view of the imbalance between relevant and irrelevant examples, two variants of DBCS are proposed respectively by considering the two-sided and onesided metrics of concept distributions. Specifically, the impact of positive and negative concepts toward search is explicitly considered. DBCS is found to be appropriate for both automatic and interactive video search. Using TRECVID 2008 video dataset for experiments, improvements of 50% and 3...

• Type: Conference
• Year: 2009
• Where: MM
• Authors: Juan Cao, HongFang Jing, Chong-Wah Ngo, Yongdong Zhang
• Keywords: Concept-based Video Retrieval, MM 2009, Multimedia, Query-to-concept Mapping, Relevant Shots