Visually Searching the Web for Content
IEEE MultiMedia
Building a visual ontology for video retrieval
Proceedings of the 13th annual ACM international conference on Multimedia
Modal keywords, ontologies, and reasoning for video understanding
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing
Building a comprehensive ontology to refine video concept detection
Proceedings of the international workshop on Workshop on multimedia information retrieval
Ontology-enriched semantic space for video search
Proceedings of the 15th international conference on Multimedia
Video Annotation System Based on Categorizing and Keyword Labelling
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
A novel learning approach to multiple tasks based on boosting methodology
Pattern Recognition Letters
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Boosting for transfer learning from multiple data sources
Pattern Recognition Letters
Annotation of endoscopic videos on mobile devices: a bottom-up approach
Proceedings of the 4th ACM Multimedia Systems Conference
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Most existing systems for content-based video retrieval (CBVR) are now amenable to support automatic low-level video content analysis and feature extraction, but they have limited effectiveness from a user's perspective. To support semantic video retrieval via keywords, we have proposed a novel framework by incorporating the concept ontology to enable more effective modeling and representation of semantic video concepts. Specifically, this novel framework includes: (a) Using the salient objects to achieve a middle-level understanding of the semantics of video contents; (b) Building a domain dependent concept ontology to enable multi-level modeling and representation of semantic video concepts; (c) Developing a multi-task boosting technique to achieve hierarchical video classifier training for automatic multi-level video annotation. The experimental results in a certain domain of medical education videos are also provided.