Boosting landmark retrieval baseline with burstiness detection

Abstract

In image retrieval, the bag-of-visual-words model-based approaches combined withthe spatial verification (SP) post-processing step have achieved considerableprogress. However, in practice, especially for retrieving landmark images, theauthors have observed that this baseline suffers from the problem of burst matches.This issue is caused by repetitive visual patterns that appear frequently amongimages. Local features derived from these burst patterns can redundantly matchothers, resulting in many invalid matches that vote over-estimated similarity scoresfor irrelevant images. Essentially, this problem can be mainly attributed to tworeasons, (i) the non-exclusive matching leads to one-to-many matches, (ii) the SPfails to filter burst matches that are closely located. To tackle this problem, aburstiness detection approach using geometric and visual word information of localfeatures is proposed. Firstly, a geometric filtering strategy is employed to removematches that are not consistent with global scale variation. Then, the one-to-onematching strategy is applied to detect and eliminate one-to-many matches. Finally,a down-weighting burstiness strategy is adopted to penalise the voting weight ofburst matches. Experimental results on three public datasets demonstrate that theproposed approach can achieve a comparable or even better accuracy over otherpopular approaches.