An Evaluation of Two Automatic Landmark Building Discovery Algorithms for City Reconstruction

  • Tobias Weyand
  • Jan Hosang
  • Bastian Leibe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


An important part of large-scale city reconstruction systems is an image clustering algorithm that divides a set of images into groups that should cover only one building each. Those groups then serve as input for structure from motion systems. A variety of approaches for this mining step have been proposed recently, but there is a lack of comparative evaluations and realistic benchmarks. In this work, we want to fill this gap by comparing two state-of-the-art landmark mining algorithms: spectral clustering and min-hash. Furthermore, we introduce a new large-scale dataset for the evaluation of landmark mining algorithms consisting of 500k images from the inner city of Paris. We evaluate both algorithms on the well-known Oxford dataset and our Paris dataset and give a detailed comparison of the clustering quality and computation time of the algorithms.


Ground Truth Visual Word Spectral Cluster Query Expansion Seed Image 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tobias Weyand
    • 1
  • Jan Hosang
    • 1
  • Bastian Leibe
    • 1
  1. 1.UMIC Research CentreRWTH Aachen UniversityGermany

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