Optimization on active learning strategy for object category retrieval

Active learning is a framework that 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 effecient using as few feedback iterations as possible. Scalability is the major problem for that online 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 that limitation. Our technique exploits ultra fast retrieval methods like 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.

• Type: Journal
• Year: 2009
• Where: ICIP
• Authors: David Gorisse, Matthieu Cord, Frédéric Precioso
• Keywords: Active Learning, Content-based Image Retrieval, ICIP 2009, Image Processing, Image Retrieval