Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Editorial: content-based image and video retrieval
Signal Processing - Special section on content-based image and video retrieval
Adaptive relevance feedback based on Bayesian inference for image retrieval
Signal Processing - Special section on content-based image and video retrieval
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
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Active learning with statistical models
Journal of Artificial Intelligence Research
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
COLT'07 Proceedings of the 20th annual conference on Learning theory
Selective sampling based on dynamic certainty propagation for image retrieval
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Leveraging active learning for relevance feedback using an information theoretic diversity measure
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
DAML: Domain Adaptation Metric Learning
IEEE Transactions on Image Processing
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Relevance feedback is an effective approach to improve the performance of image retrieval by leveraging the labeling of human. In order to alleviate the burden of labeling, active learning method has been introduced to select the most informative samples for labeling. In this paper, we present a novel batch mode active learning scheme for informative sample selection. Inspired by the method of graph propagation, we not only take the correlation between labeled samples and unlabeled samples, but the correlation among unlabeled samples taken into account as well. Especially, considering the unbalanced distribution of samples and the personalized feedback of human we propose an asymmetric propagation scheme to unify the various criteria including uncertainty, diversity and density into batch mode active learning in relevance feedback. Extensive experiments on publicly available datasets show that the proposed method is promising.