Scalable active learning strategy for object category retrieval

Since the digital revolution, the volume of images to be processed has grown exponentially. Interactive search systems have to deal with these huge databases to remain effective. As the complexity of on-line learning methods is at least linear in the size of the database, scalability is the major problem for these methods. Fast retrieval systems, with index structures for fast navigation, have hence become like a Holy Grail. In this article, we propose a strategy to overcome this scalability limitation. Our technique exploits ultra fast retrieval methods as Locally Sensitive Hashing to speed up active learning system. Experiments on database of 180 K images are reported. The results show that our method is 45 times faster than state of the art approaches for similar accuracy.