Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
IEEE Transactions on Image Processing
A content-based image retrieval system for fish taxonomy
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Proceedings of the 2009 ACM symposium on Applied Computing
Hi-index | 0.00 |
The success of the relevance feedback search paradigm in image retrieval is influenced by the selection strategy employed by the system to choose the images presented to the user for providing feedback. Indeed, this strategy has a strong effect on the transfer of information between the user and the system. Using SVMs, we put forward a new active learning selection strategy that minimizes redundancy between the examples. We focus on region-based image retrieval and we expect our approach to produce better results than existing selection strategies. Experimental evidence in the context of generalist image databases confirms the efectiveness of this selection strategy.