Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Evaluation of active learning strategies for video indexing
Image Communication
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Multi-criteria-based strategy to stop active learning for data annotation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Semi-Supervised Learning
Active learning with sampling by uncertainty and density for data annotations
IEEE Transactions on Audio, Speech, and Language Processing
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
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According to some certain criteria, active learning algorithm selects the most informative samples from the unlabeled sample sets for human experts to label, then the labeled samples, called the training set, are used to train a model for image classification or image annotation. In this way, it not only decreases the efforts of manual labeling randomly, but also reduces the sample complexity and may speeds up the learning process of image classification model. After many years developed and researched, we have accumulated fruitful research results in active learning. In this paper, we make a literature survey of active learning in multimedia annotation and retrieval. Firstly, we briefly introduce the basic principle of active learning and then analyze some sample selection strategies. Furthermore, we introduce the state of the art of active learning algorithms, which include the combination of active learning with semi-supervised learning, multi-label learning, multi-instance learning and incremental learning respectively. Finally, we throw out some open problems on active learning.