An evaluation of two automatic landmark building discovery algorithms for city reconstruction

  • Authors:

  • Tobias Weyand;Jan Hosang;Bastian Leibe

  • Affiliations:

  • UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany;UMIC Research Centre, RWTH Aachen University, Germany

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
  • Year:
  • 2010

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Abstract

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.