Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
RETIN: a smart interactive digital media retrieval system
Proceedings of the 6th ACM international conference on Image and video retrieval
High-dimensional descriptor indexing for large multimedia databases
Proceedings of the 17th ACM conference on Information and knowledge management
SALSAS: Sub-linear active learning strategy with approximate k-NN search
Pattern Recognition
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Active learning is a machine learning technique which has attracted a lot of research interest in the content-based image retrieval (CBIR) in recent years. To be effective, an active learning system must be fast and efficient using as few (relevance) feedback iterations as possible. Scalability is the major problem for such an on-line learning method, since the complexity of such methods on a database of size n is in the best case O(n * log(n)). In this article we propose a strategy to overcome this limitation. Our technique exploits ultra fast retrieval methods like Locality Sensitive Hashing (LSH), recently applied for unsupervised image retrieval. Combined with active selection, our method is able to achieve very fast active learning task in very large database. Experiments on VOC2006 database are reported, results are obtained four times faster while preserving the accuracy.