Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Sequential association mining for video summarization
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Effective Video Annotation by Mining Visual Features and Speech Features
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 01
Mining video associations for efficient database management
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Characteristic pattern discovery in videos
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Hi-index | 0.00 |
In this paper, we propose a novel approach for semantic video annotation through integrating visual features and speech features. By employing statistics and association patterns, the relations between video shots and human concept can be discovered effectively to conceptualize videos. In other words, the utilization of high-level rules can effectively complement the insufficiency of statistics-based methods in dealing with broad and complex keyword identification in video annotation. Empirical evaluations on NIST TRECVID video datasets reveal that our proposed approach can enhance the annotation accuracy substantially.