Automatic parsing and indexing of news video
Multimedia Systems
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Determining computable scenes in films and their structures using audio-visual memory models
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Principles of visual information retrieval
Principles of visual information retrieval
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
Toward automatic extraction of expressive elements from motion pictures: tempo
IEEE Transactions on Multimedia
ClassView: hierarchical video shot classification, indexing, and accessing
IEEE Transactions on Multimedia
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Concept-oriented video skimming and adaptation via semantic classification
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
An online-optimized incremental learning framework for video semantic classification
Proceedings of the 12th annual ACM international conference on Multimedia
To construct optimal training set for video annotation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Optimizing training set construction for video semantic classification
EURASIP Journal on Advances in Signal Processing
Semi-supervised multi-instance multi-label learning for video annotation task
Proceedings of the 20th ACM international conference on Multimedia
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Digital video now plays an important role in medical education and healthcare, but our ability to automatic video indexing at semantic level is currently primitive. In this paper, we propose a novel framework to enable more effective semantic video classification and indexing in a specific surgery education video domain. Specifically, this framework includes: (a) A novel semantic-sensitive video content characterization and representation framework by using principal video shots and their perceptual multimodal features. (b) A novel semantic medical concept interpretation technique by using flexible mixture model. (c) A semantic video classifier 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). Since more effective video content characterization framework has been integrated with an adaptive EM algorithm for video classification, our semantic video classifier has improved the classification accuracy significantly. For skin classification, its accuracy is close to 95.5%. For semantic surgical video classification, it achieves overall ≈ 84.6% accuracy.