  To effectively retrieve objects from large corpus with high accuracy is a
challenge task. In this paper, we propose a method that propagates visual
feature level similarities on a Markov random field (MRF) to obtain a high
level correspondence in image space for image pairs. The proposed
correspondence between image pair reflects not only the similarity of low-level
visual features but also the relations built through other images in the
database and it can be easily integrated into the existing
bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate
our method on the standard Oxford-5K, Oxford-105K and Paris-6K dataset. The
experiment results show that the proposed method significantly improves the
retrieval accuracy on three datasets and exceeds the current state-of-the-art
retrieval performance.
