Active Congruency-Based Reranking

Abstract

We present a tool for re-ranking the results of a specific query by considering the matrix of pairwise similarities among the elements of the set of retrieved results and the query itself. The re-ranking, thus, makes use of the similarities between the various results and does not employ additional sources of information. The tool is based on graphical Bayesian models, which reinforce retrieved items strongly linked to other retrievals, and on repeated clustering to measure the stability of the obtained associations. To this, we add an active relevance-based re-ranking process in order to leverage true matches, which have very low similarity to the query. The utility of the tool is demonstrated within the context of a visual search of documents from the Cairo Genizah. It is also demonstrated in a completely different domain or retrieving, given an input image of a painting, other related paintings.