HMNews: a multimodal news data association framework
Proceedings of the 2010 ACM Symposium on Applied Computing
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
A Bayesian network modeling approach for cross media analysis
Image Communication
Image clustering fusion technique based on BFS
Proceedings of the 20th ACM international conference on Information and knowledge management
Frame filtering and path verification for improving video copy detection
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
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Analysis on click-through data from a very large search engine log shows that users are usually interested in the top-ranked portion of returned search results. Therefore, it is crucial for search engines to achieve high accuracy on the top-ranked documents. While many methods exist for boosting video search performance, they either pay less attention to the above factor or encounter difficulties in practical applications. In this paper, we present a flexible and effective reranking method, called CR-Reranking, to improve the retrieval effectiveness. To offer high accuracy on the top-ranked results, CR-Reranking employs a cross-reference (CR) strategy to fuse multimodal cues. Specifically, multimodal features are first utilized separately to rerank the initial returned results at the cluster level, and then all the ranked clusters from different modalities are cooperatively used to infer the shots with high relevance. Experimental results show that the search quality, especially on the top-ranked results, is improved significantly.