Image Ranking with Eye Movements

Abstract

In order to help users navigate an image search system, one could provide explicit rank information on a set of images. These rankings are learnt so to present a new set of relevant images. Although, requiring explicit information may not be feasible in some cases, we consider the setting where the user provides implicit feedback, eye movements, to assist in such a task. This paper explores the idea of implicitly incorporating eye movement features in an image ranking task. Previous work had demonstrated that combining eye movement and image features improved the retrieval accuracy. Despite promising results the proposed approach is unrealistic as no eye movements are given a-priori for new images. We propose a novel search approach which combines image together with eye movements features in a tensor Ranking Support Vector Machine, and show that by extracting the individual source-specific weight vectors we are able to construct a new image-based semantic space which outperforms in retrieval accuracy.

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