Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An active feedback framework for image retrieval
Pattern Recognition Letters
Relevance feedback strategies for artistic image collections tagging
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Feature space warping relevance feedback with transductive learning
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Using relevance feedback to bridge the semantic gap
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
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Retrieval techniques based on pure similarity metrics are often suffered from the scales of image features. An alternative approach is to learn a mapping based on queries and relevance feedback by supervised learning. However, the learning is plagued by the insufficiency of labeled training images. Different from most current research in image retrieval, this paper investigates the possibility of taking advantage of unlabeled images in the given image database to make feasible a hybrid statistical learning. Assuming a generative model of the database, the proposed approach casts image retrieval as a transductive learning problem in a probabilistic framework. Our experiments show that the proposed approach has a satisfactory performance in image retrieval applications.