A stochastic framework for optimal key frame extraction from MPEG video databases
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Fusing MPEG-7 visual descriptors for image classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents an approach on high-level feature detection within video documents, using a Region Thesaurus. A video shot is represented by a single keyframe and MPEG-7 features are extracted locally, from coarse segmented regions. Then a clustering algorithm is applied on those extracted regions and a region thesaurus is constructed to facilitate the description of each keyframe at a higher level than the low-level descriptors but at a lower than the high-level concepts. A model vector representation is formed and several high-level concept detectors are appropriately trained using a global keyframe annotation. The proposed approach is thoroughly evaluated on the TRECVID 2007 development data for the detection of nine high level concepts, demonstrating sufficient performance on large data sets.