Geo-distinctive Visual Element Matching for Location Estimation of Images

TL;DR

This paper introduces DVEM, a novel image representation and matching method that enhances visual-based location estimation by emphasizing geo-distinctive visual elements, leading to significant performance improvements on large-scale datasets.

Contribution

The paper presents a new approach called distinctive visual element matching (DVEM) that uses query-specific visual representations and geo-distinctiveness to improve location estimation accuracy.

Findings

DVEM outperforms existing methods on large-scale datasets.

The approach effectively captures geo-distinctive visual elements.

Results demonstrate significant accuracy improvements.

Abstract

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 common sense 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.