  This paper describes PinView, a content-based image retrieval system that
exploits implicit relevance feedback collected during a search session. PinView
contains several novel methods to infer the intent of the user. From relevance
feedback, such as eye movements or pointer clicks, and visual features of
images, PinView learns a similarity metric between images which depends on the
current interests of the user. It then retrieves images with a specialized
online learning algorithm that balances the tradeoff between exploring new
images and exploiting the already inferred interests of the user. We have
integrated PinView to the content-based image retrieval system PicSOM, which
enables applying PinView to real-world image databases. With the new algorithms
PinView outperforms the original PicSOM, and in online experiments with real
users the combination of implicit and explicit feedback gives the best results.
