A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selective Sampling with Co-Testing: Preliminary Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Content-Based Ima e Orientation Detection with Support Vector Machines
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
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
Efficient semantic annotation method for indexing large personal video database
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
To construct optimal training set for video annotation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Manifold-ranking based video concept detection on large database and feature pool
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic video annotation by semi-supervised learning with kernel density estimation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Optimizing multi-graph learning: towards a unified video annotation scheme
Proceedings of the 15th international conference on Multimedia
Optimizing training set construction for video semantic classification
EURASIP Journal on Advances in Signal Processing
Transductive multi-label learning for video concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Semi-supervised kernel density estimation for video annotation
Computer Vision and Image Understanding
Multimedia content personalization based on peer-level annotation
Proceedings of the seventh european conference on European interactive television conference
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Improving video concept detection using spatio-temporal correlation
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Personalization in multimedia retrieval: A survey
Multimedia Tools and Applications
A transductive multi-label learning approach for video concept detection
Pattern Recognition
A novel framework for concept detection on large scale video database and feature pool
Artificial Intelligence Review
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In this paper, we will propose a novel semi-automatic annotation scheme for video semantic classification. It is well known that the large gap between high-level semantics and low-level features is difficult to be bridged by full-automatic content analysis mechanisms. To narrow down this gap, relevance feedback has been introduced in a number of literatures, especially in those works addressing the problem of image retrieval. And at the same time, active learning is also suggested to accelerate the converging speed of the learning process by labeling the most informative samples. Generally an active learning scheme includes a sample selection engine and a learning engine. In this paper, we will discuss the limitations of existing active learning algorithms and propose a novel active learning scheme based on multiple complementary predictors and incremental model adaptation, which improves the efficiencies of both of the primary components of active learning. Firstly, an efficient sample selection scheme is proposed, in which multiple predictors are applied to find most informative samples. Then an incremental model adaptation technique, maximum likelihood linear regression (MLLR), is used to update the classifiers which tackle the issue of unbalance between the original training set and the newly labeled data. It is proved that the samples selected by the proposed scheme are more representative than general active learning scheme, as well as the incremental model adaptation scheme is effective especially when the newly added data size is small.