Does relevance feedback improve document retrieval performance?
SIGIR '78 Proceedings of the 1st annual international ACM SIGIR conference on Information storage and retrieval
Integrating Unlabeled Images for Image Retrieval Based on Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Content-based sub-image retrieval using relevance feedback
Proceedings of the 2nd ACM international workshop on Multimedia databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Query Point Movement Techniques for Large CBIR Systems
IEEE Transactions on Knowledge and Data Engineering
Graph-based transductive learning for robust visual tracking
Pattern Recognition
Picture extraction from digitized historical manuscripts
Proceedings of the ACM International Conference on Image and Video Retrieval
Mean shift feature space warping for relevance feedback
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Toward consistent evaluation of relevance feedback approaches in multimedia retrieval
AMR'05 Proceedings of the Third international conference on Adaptive Multimedia Retrieval: user, context, and feedback
Interactive Search by Direct Manipulation of Dissimilarity Space
IEEE Transactions on Multimedia
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Relevance feedback is a widely adopted approach to improve content-based information retrieval systems by keeping the user in the retrieval loop. Among all, the feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. Recently, combination of feature space warping and query point movement techniques has been proposed in contrast to learning based approaches, showing good performance under different data distributions. In this paper we propose to merge feature space warping and transductive learning, in order to benefit from both the ability of adapting data to the user hints and the information coming from unlabeled samples. Experimental results on an image retrieval task reveal significant performance improvements from the proposed method.