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
Active learning using adaptive resampling
Proceedings of the sixth 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
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth 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
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active learning using pre-clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Proceedings of the 13th annual ACM international conference on Multimedia
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
Extreme video retrieval: joint maximization of human and computer performance
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Fusing semantics, observability, reliability and diversity of concept detectors for video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Active learning with statistical models
Journal of Artificial Intelligence Research
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Distribution-based concept selection for concept-based video retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Selection of Concept Detectors for Video Search by Ontology-Enriched Semantic Spaces
IEEE Transactions on Multimedia
Concept-Driven Multi-Modality Fusion for Video Search
IEEE Transactions on Circuits and Systems for Video Technology
Enhancing image retrieval by an exploration-exploitation approach
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A multimedia analytics framework for browsing image collections in digital forensics
Proceedings of the 20th ACM international conference on Multimedia
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Active learning with uncertainty sampling has been popularly employed in implementing interactive video search, due to its promise to reduce labeling efforts. However, since the ultimate goal of interactive search is to find as many relevant shots as possible, the purely explorative learning strategy always places conventional active learning in a dilemma whether to explore uncertain areas for a better understanding of query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel paradigm of active learning, where a coaching process is introduced to guide the leaner by jointly consulting an estimated prior query distribution and a posterior query distribution indicated by current classifier outcomes. To bypass the difficulty of estimating the prior query distribution from a limited number of labeled relevant instances, we propose to estimate the distribution using a set of semantic distributions which are statistically from the same distributions as the labeled relevant instances. With the coaching of both prior and posterior query distributions, the learning can be conducted and scheduled with a global perspective, and thus can explicitly balance the trade-off between exploitation and exploration. The results of the experiments on TRECVID 2005--2009 datasets validate the efficiency and effectiveness of our approach, which outperforms the conventional active learning methods with uncertainty sampling and also shows superiority to several state-of-the art interactive video search systems.