Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
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
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rapid and brief communication: Active learning for image retrieval with Co-SVM
Pattern Recognition
Active learning with multiple views
Journal of Artificial Intelligence Research
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In relevance feedback of image retrieval, selective sampling is often used to alleviate the burden of labeling by selecting only the most informative data to label. Traditional data selection scheme often selects a batch of data at a time and label them all together, which neglects the data's correlation and thus jeopardizes the effectiveness. In this paper, we propose a novel Dynamic Certainty Propagation (DCP) scheme for informative data selection. For each unlabeled data, we define the notion of certainty to quantify our confidence in its predicted label. Every time, we only label one single data point with the lowest degree of certainty. Then we update the rest unlabeled data's certainty dynamically according to their correlation. This one-by-one labeling offers us extra guidance from the last labeled data for the next labeling. Experiments show that the DCP scheme outperforms the traditional method evidently.