Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval

In content-based image retrieval (CBIR) with relevance feedback we would like to retrieve relevant images based on their content features and the feedback given by users. In this paper we view CBIR as an Exploration-Exploitation problem and apply a kernel version of the LINREL algorithm to solve it. By using multiple feature extraction methods and utilising the feedback given by users, we adopt a strategy of multiple kernel learning to find a relevant feature space for the kernel LINREL algorithm. We call this algorithm LINRELMKL. Furthermore, when we have access to eye movement data of users viewing images we can enrich our (multiple) feature spaces by using a tensor kernel SVM. When learning in this enriched space we show that we can significantly improve the search results over the LINREL and LINRELMKL algorithms. Our results suggest that the use of exploration-exploitation with multiple feature spaces is an efficient way of constructing CBIR systems, and that when eye movement features are available, they should be used to help improve CBIR.