Exploiting contextual information for image re-ranking and rank aggregation

Abstract

Content-based image retrieval (CBIR) systems aim to retrieve the most similar images in a collection, given a query image. Since users are interested in the returned images placed at the first positions of ranked lists (which usually are the most relevant ones), the effectiveness of these systems is very dependent on the accuracy of ranking approaches. This paper presents a novel re-ranking algorithm aiming to exploit contextual information for improving the effectiveness of rankings computed by CBIR systems. In our approach, ranked lists and distance scores are used to create context images, later used for retrieving contextual information. We also show that our re-ranking method can be applied to other tasks, such as (a) combining ranked lists obtained using different image descriptors (rank aggregation) and (b) combining post-processing methods. Conducted experiments involving shape, color, and texture descriptors and comparisons with other post-processing methods demonstrate the effectiveness of our method.