Title

Image Geo-Localization Based On Multiplenearest Neighbor Feature Matching Usinggeneralized Graphs

Keywords

feature correspondence; feature matching; generalized graphs; Generalized Minimum Clique Problem (GMCP); generalized minimum spanning tree (GMST); Geo-location; image localization; multiple nearest neighbor feature matching

Abstract

In this paper, we present a new framework for geo-locating an image utilizing a novel multiple nearest neighbor feature matching method using Generalized Minimum Clique Graphs (GMCP). First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set. Next, we apply our GMCP-based feature matching to select a single nearest neighbor for each query feature such that all matches are globally consistent. Our approach to feature matching is based on the proposition that the first nearest neighbors are not necessarily the best choices for finding correspondences in image matching. Therefore, the proposed method considers multiple reference nearest neighbors as potential matches and selects the correct ones by enforcing consistency among their global features (e.g., GIST) using GMCP. In this context, we argue that using a robust distance function for finding the similarity between the global features is essential for the cases where the query matches multiple reference images with dissimilar global features. Towards this end, we propose a robust distance function based on the Gaussian Radial Basis Function (G-RBF). We evaluated the proposed framework on a new data set of 102k street view images; the experiments show it outperforms the state of the art by 10 percent. © 1979-2012 IEEE.

Publication Date

1-1-2014

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

36

Issue

8

Number of Pages

1546-1558

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2014.2299799

Socpus ID

84904215449 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84904215449

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