Digital Image Processing
CMVF: a novel dimension reduction scheme for efficient indexing in a large image database
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Semi-Supervised Cross Feature Learning for Semantic Concept Detection in Videos
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Orthogonal Neighborhood Preserving Projections
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
Introduction to Information Retrieval
Introduction to Information Retrieval
Transductive multi-label learning for video concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework
IEEE Transactions on Multimedia
Evaluation of automatic shot boundary detection on a large video test suite
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
Event detection in field sports video using audio-visual features and a support vector Machine
IEEE Transactions on Circuits and Systems for Video Technology
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With explosive amounts of video data emerging from the Internet, automatic video concept detection is becoming very important and has been received great attention. However, reported approaches mainly suffer from low identification accuracy and poor robustness over different concepts. One of the main reason is that the existing approaches typically isolate the video signature generation from the process of classifier training. Also, very few approaches consider effects of multiple video features. The paper describes a novel approach fusing different information from diverse knowledge sources to facilitate effective video concept detection. The system is designed based on CM*F scheme [7], [5] and its basic architecture contains two core components including 1) CM*F based video signature generation scheme and 2) CM*F based video concept detector. To evaluate the approach proposed, an extensive experimental study on two large video databases has been carried out. The results demonstrate the superiority of the method in terms of effectiveness and robustness.