On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Influence of speech recognition errors on topic detection (poster session)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Structured use of external knowledge for event-based open domain question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
The use of topic evolution to help users browse and find answers in news video corpus
Proceedings of the 15th international conference on Multimedia
VisionGo: Towards video retrieval with joint exploration of human and computer
Information Sciences: an International Journal
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Precise automated video search is gaining in importance as the amount of multimedia information is increasing at exponential rates. One of the drawbacks that make video retrieval difficult is the lack of available semantics. In this paper, we propose to supplement the semantic knowledge for retrieval by providing useful semantic clusters derived from event entities present in the news video. These entities include the output from keywords derived from the automated speech recognition (ASR) and event-related High-level Features (HLF) extracted from the news video at the pseudo story level. Fuzzy clustering is then carried out to group similar stories together to form semantic clusters. The retrieval system utilizes these clusters to refine the re-ranking process in the Pseudo Relevance Feedback (PRF) step. Initial experiments performed on video search task using the TRECVID 2005 dataset show that the proposed approach can improve the search performance significantly.