A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Assessing concept selection for video retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Can High-Level Concepts Fill the Semantic Gap in Video Retrieval? A Case Study With Broadcast News
IEEE Transactions on Multimedia
Hierarchical feedback algorithm based on visual community discovery for interactive video retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Towards hierarchical context: unfolding visual community potential for interactive video retrieval
Multimedia Tools and Applications
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Correlation-Based Ranking for Large-Scale Video Concept Retrieval
International Journal of Multimedia Data Engineering & Management
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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 34% are reported when compared to text-based and visual-based query-to concept mapping respectively in automatic search. Meanwhile, DBCS shows continuous improvement for all rounds of user feedbacks in interactive search.