Geo-Distinctive Visual Element Matching for Location Estimation of Images

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
71 Downloads (Pure)


We propose an image representation and matching approach that substantially improves visual-based location estimation for images. The main novelty of the approach, called distinctive visual element matching (DVEM), is its use of representations that are specific to the query image whose location is being predicted. These representations are based on visual element clouds, which robustly capture the connection between the query and visual evidence from candidate locations. We then maximize the influence of visual elements that are geo-distinctive because they do not occur in images taken at many other locations. We carry out experiments and analysis for both geo-constrained and geo-unconstrained location estimation cases using two large-scale, publicly available datasets: the San Francisco Landmark dataset with 1.06 million street-view images and the MediaEval'15 Placing Task dataset with 5.6 million geo-tagged images from Flickr. We present examples that illustrate the highly transparent mechanics of the approach, which are based on commonsense observations about the visual patterns in image collections. Our results show that the proposed method delivers a considerable performance improvement compared to the state-of-the-art.

Original languageEnglish
Pages (from-to)1179-1194
Number of pages16
JournalIEEE Transactions on Multimedia
Issue number5
Publication statusPublished - 2018


  • Geo-location Estimation
  • information retrieval
  • large scale image retrieval

Fingerprint Dive into the research topics of 'Geo-Distinctive Visual Element Matching for Location Estimation of Images'. Together they form a unique fingerprint.

Cite this