Visual Geo Localization And Location Aware Image Understanding
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Visual Geo-localization and Location-aware Image Understanding
Author | : Amir Roshan Zamir |
Publisher | : |
Total Pages | : 143 |
Release | : 2014 |
Genre | : |
ISBN | : |
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Geo-localization is the problem of discovering the location where an image or video was captured. Recently, large scale geo-localization methods which are devised for ground-level imagery and employ techniques similar to image matching have attracted much interest. In these methods, given a reference dataset composed of geo-tagged images, the problem is to estimate the geo-location of a query by finding its matching reference images. In this dissertation, we address three questions central to geo-spatial analysis of ground-level imagery: 1) How to geo-localize images and videos captured at unknown locations? 2) How to refine the geo-location of already geo-tagged data? 3) How to utilize the extracted geo-tags? 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 the consistency among their global features (e.g., GIST) using GMCP. Our evaluations using a new data set of 102k Street View images shows the proposed method outperforms the state-of-the-art by 10 percent.
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