Multi-video synopsis for video representation
Signal Processing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Probabilistic temporal multimedia data mining
ACM Transactions on Intelligent Systems and Technology (TIST)
Contextual Video Recommendation by Multimodal Relevance and User Feedback
ACM Transactions on Information Systems (TOIS)
Beyond search: Event-driven summarization for web videos
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Tag-based social image search with visual-text joint hypergraph learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
k-Partite graph reinforcement and its application in multimedia information retrieval
Information Sciences: an International Journal
Biview face recognition in the shape-texture domain
Pattern Recognition
Multimedia encyclopedia construction by mining web knowledge
Signal Processing
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Event detection is one of the most fundamental components for various kinds of domain applications of video information system. In recent years, it has gained a considerable interest of practitioners and academics from different areas. While detecting video event has been the subject of extensive research efforts recently, much less existing approach has considered multimodal information and related efficiency issues. In this paper, we use a subspace selection technique to achieve fast and accurate video event detection using a subspace selection technique. The approach is capable of discriminating different classes and preserving the intramodal geometry of samples within an identical class. With the method, feature vectors presenting different kind of multi data can be easily projected from different identities and modalities onto a unified subspace, on which recognition process can be performed. Furthermore, the training stage is carried out once and we have a unified transformation matrix to project different modalities. Unlike existing multimodal detection systems, the new system works well when some modalities are not available. Experimental results based on soccer video and TRECVID news video collections demonstrate the effectiveness, efficiency and robustness of the proposed MMP for individual recognition tasks in comparison to the existing approaches.