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Location Recognition Using Prioritized Feature Matching

  • Yunpeng Li
  • Noah Snavely
  • Daniel P. Huttenlocher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

We present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections. Such recovered structure contains a significant amount of useful information about images and image features that is not available when considering images in isolation. For instance, we can predict which views will be the most common, which feature points in a scene are most reliable, and which features in the scene tend to co-occur in the same image. Based on this information, we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization. Our approach is based on considering features in the scene database, and matching them to query image features, as opposed to more conventional methods that match image features to visual words or database features. We find this approach results in improved performance, due to the richer knowledge of characteristics of the database features compared to query image features. We present experiments on two large city-scale photo collections, showing that our algorithm compares favorably to image retrieval-style approaches to location recognition.

Keywords

Point Cloud Image Retrieval Query Image Visibility Graph Sift Descriptor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

978-3-642-15552-9_57_MOESM1_ESM.pdf (4.7 mb)
Electronic Supplementary Material (4,794 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yunpeng Li
    • 1
  • Noah Snavely
    • 1
  • Daniel P. Huttenlocher
    • 1
  1. 1.Department of Computer ScienceCornell UniversityIthaca

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