Automated Analysis of Nursing Home Observations
IEEE Pervasive Computing
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
To construct optimal training set for video annotation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing training set construction for video semantic classification
EURASIP Journal on Advances in Signal Processing
Online multi-label active annotation: towards large-scale content-based video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Effective multi-label active learning for text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised active learning based on hierarchical graph-theoretic clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-view multi-label active learning for image classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A novel traffic analysis for identifying search fields in the long tail of web sites
Proceedings of the 19th international conference on World wide web
A discriminative learning framework with pairwise constraints for video object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Cost-Sensitive Active Visual Category Learning
International Journal of Computer Vision
An effective procedure exploiting unlabeled data to build monitoring system
Expert Systems with Applications: An International Journal
Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
International Journal of Computer Vision
Multimedia Tools and Applications
Guess what? a game for affective annotation of video using crowd sourcing
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Coached active learning for interactive video search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Margin-Based active learning for structured output spaces
ECML'06 Proceedings of the 17th European conference on Machine Learning
On active learning in hierarchical classification
Proceedings of the 21st ACM international conference on Information and knowledge management
Active learning via neighborhood reconstruction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Active learning with multi-label SVM classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Labeling video data is an essential prerequisite for many visionapplications that depend on training data, such as visualinformation retrieval, object recognition, and human activitymodeling. However, manually creating labels is not onlytime-consuming but also subject to human errors, and eventually,becomes impossible for a very large amount of data (e.g. 24/7surveillance video). To minimize the human effort in labeling, wepropose a unified multi-class active learning approach forautomatically labeling video data. The contributions of this paperinclude extending active learning from binary classes to multipleclasses and evaluating several practical sample selectionstrategies. The experimental results show that the proposedapproach works effectively even with a significantly reduced amountof labeled data. The best sample selection strategy can achievemore than a 50% error reduction over random sample selection.