Automatic parsing and indexing of news video
Multimedia Systems
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
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MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
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CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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IEEE Transactions on Multimedia
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing
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IEEE Transactions on Circuits and Systems for Video Technology
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
Large scale news video database browsing and retrieval via information visualization
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Multimedia news exploration and retrieval by integrating keywords, relations and visual features
Multimedia Tools and Applications
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Concept-oriented video skimming and adaptation plays an important role in enabling online medical education by selecting and transmitting the suitable medical video clips to the students over network. In this paper, we propose a novel framework to enable concept-oriented video skimming and adaptation in a specific domain of medical education video. Specifically, this framework includes: (a) A novel semantic-sensitive framework for video content characterization and representation by using principal video shots to enhance the quality of features on discriminating between different semantic video concepts. (b) A novel technique for semantic medical concept interpretation by using finite mixture models to approximate the class distributions of the relevant principal video shots. (c) A novel classifier training scheme by using an adaptive Expectation-Maximization (EM) algorithm for automatic parameter estimation and model selection (i.e., selecting the optimal number of mixture Gaussian components). (d) Subjective driven concept-oriented video skimming algorithm via semantic video classification