Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting unlabeled data in ensemble methods
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
An online-optimized incremental learning framework for video semantic classification
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Mining multimedia salient concepts for incremental information extraction
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in 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
Active learning with statistical models
Journal of Artificial Intelligence Research
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
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As there is a large gap between high-level semantics and low-level features, it is difficult to automatically obtain high-accuracy video semantic annotation through general statistical learning based methods. In this paper, we propose a novel annotation framework based on active learning and semi-supervised ensemble method, which is specially designed for personal video database. To efficiently annotate the home video database, an initial training set is first elaborately constructed based on the distribution analysis of the entire video dataset. Then, both a semi-supervised ensemble based method and an active learning based method are proposed, which aims at minimizing a margin cost function of ensemble to ensure the generalization capacity. The experiment results on about 50 hours home videos show that the proposed method performs superior to both existing semi-supervised learning algorithms and the general active learning algorithms in terms of annotation accuracy and performance stability.