Foundations of statistical natural language processing
Foundations of statistical natural language processing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic representation: search and mining of multimedia content
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Joint visual-text modeling for automatic retrieval of multimedia documents
Proceedings of the 13th annual ACM international conference on Multimedia
The use and utility of high-level semantic features in video retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Video search in concept subspace: a text-like paradigm
Proceedings of the 6th ACM international conference on Image and video retrieval
The importance of query-concept-mapping for automatic video retrieval
Proceedings of the 15th international conference on Multimedia
Learning structured concept-segments for interactive video retrieval
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A probabilistic ranking framework using unobservable binary events for video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Query representation by structured concept threads with application to interactive video retrieval
Journal of Visual Communication and Image Representation
Concept detectors: how good is good enough?
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Reusing annotation labor for concept selection
Proceedings of the ACM International Conference on Image and Video Retrieval
Simulating the future of concept-based video retrieval under improved detector performance
Multimedia Tools and Applications
The uncertain representation ranking framework for concept-based video retrieval
Information Retrieval
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Extraction and utilization of high-level semantic features are critical for more effective video retrieval. However, the performance of video retrieval hasn't benefited much despite of the advances in high-level feature extraction. To make good use of high-level semantic features in video retrieval, we present a method called pointwise mutual information weighted scheme(PMIWS). The method makes a good judgment of the relevance of all the semantic features to the queries, taking the characteristics of semantic features into account. The method can also be extended for the fusion of multi-modalities. Experiment results based on TRECVID2005 corpus demonstrate the effectiveness of the method.