Elements of information theory
Elements of information theory
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active Concept Learning for Image Retrieval in Dynamic Databases
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Semi-Supervised Active Learning Framework for Image Retrieval
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
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
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Evaluation of active learning strategies for video indexing
Image Communication
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A comprehensive human computation framework: with application to image labeling
MM '08 Proceedings of the 16th ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comprehensive human computation framework: with application to image labeling
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Visual categorization with negative examples for free
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Active tagging for image indexing
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Proceedings of the international conference on Multimedia
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Mining multi-tag association for image tagging
World Wide Web
Local image tagging via graph regularized joint group sparsity
Pattern Recognition
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Existing video search engines have not taken the advantages of video content analysis and semantic understanding. Video search in academia uses semantic annotation to approach content-based indexing. We argue this is a promising direction to enable real content-based video search. However, due to the complexity of both video data and semantic concepts, existing techniques on automatic video annotation are still not able to handle large-scale video set and large-scale concept set, in terms of both annotation accuracy and computation cost. To address this problem, in this paper, we propose a scalable framework for annotation-based video search, as well as a novel approach to enable large-scale semantic concept annotation, that is, online multi-label active learning. This framework is scalable to both the video sample dimension and concept label dimension. Large-scale unlabeled video samples are assumed to arrive consecutively in batches with an initial pre-labeled training set, based on which a preliminary multi-label classifier is built. For each arrived batch, a multi-label active learning engine is applied, which automatically selects and manually annotates a set of unlabeled sample-label pairs. And then an online learner updates the original classifier by taking the newly labeled sample-label pairs into consideration. This process repeats until all data are arrived. During the process, new labels, even without any pre-labeled training samples, can be incorporated into the process anytime. Experiments on TRECVID dataset demonstrate the effectiveness and efficiency of the proposed framework.