Pinview: Implicit Feedback in Content-Based Image Retrieval
Peter Auer, Zakria Hussain, Samuel Kaski, Arto Klami, Jussi Kujala, Jorma Laaksonen, Alex Leung, Kitsuchart Pasupa and John Shawe-Taylor
In: Workshop on Applications of Pattern Analysis (WAPA'2010), 1-2 September 2010, Cumberland Lodge, UK.
This paper describes Pinview, a content-based image retrieval system that exploits implicit relevance feedback during a search session. Pinview contains several novel methods that infer the intent of the user. From relevance feedback, such as eye movements or 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 reinforcement learning algorithm that balances the tradeo between exploring new images and exploiting the already inferred interests of the user. In practise, we have integrated
Pinview to the content-based image retrieval system PicSOM, in order to apply it to realworld image databases. Preliminary experiments show that eye movements provide a rich input modality from which it is possible to learn the interests of the user.