Image ranking with implicit feedback from eye movements.

In order to help users navigate an image search system, one could provide explicit information on a small set of images as to which of them are relevant or not to their task. These rankings are learned in order to present a user with a new set of images that are relevant to their task. Requiring such explicit information may not be feasible in a number of cases, we consider the setting where the user provides implicit feedback, eye movements, to assist when performing such a task. This paper explores the idea of implicitly incorporating eye movement features in an image ranking task where only images are available during testing. Previous work had demonstrated that combining eye movement and image features improved on the retrieval accuracy when compared to using each of the sources independently. Despite these encouraging results the proposed approach is unrealistic as no eye movements will be presented a-priori for new images (i.e. only after the ranked images are presented would one be able to measure a user's eye movements on them). We propose a novel search methodology which combines image features together with implicit feedback from users' eye movements in a tensor ranking Support Vector Machine and show that it is possible to extract the individual source-specific weight vectors. Furthermore, we demonstrate that the decomposed image weight vector is able to construct a new image-based semantic space that outperforms the retrieval accuracy than when solely using the image-features.