Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Event Detection and Summarization in Sports Video
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Semantic video classification by integrating flexible mixture model with adaptive EM algorithm
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Semi-automatic video annotation based on active learning with multiple complementary predictors
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Efficient semantic annotation method for indexing large personal video database
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Optimizing training set construction for video semantic classification
EURASIP Journal on Advances in Signal Processing
Video-based face tracking and recognition on updating twin GMMs
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper considers the problems of feature variation and concept uncertainty in typical learning-based video semantic classification schemes. We proposed a new online semantic classification framework, termed OOIL (for Online-Optimized Incremental Learning), in which two sets of optimized classification models, local and global, are online trained by sufficiently exploiting both local and global statistic characteristics of videos. The global models are pre-trained on a relatively small set of pre-labeled samples. And the local models are optimized for the under-test video or video segment by checking a small portion of unlabeled samples in this video, while they are also applied to incrementally update the global models. Experiments have illustrated promising results on simulated data as well as real sports videos.