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

Tobias Weyand, Jan Hosang, Bastian Leibe
European Conference on Computer Vision (ECCV'10) Workshop

An important part of large-scale city reconstruction systems is an im- age 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 mo- tion 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 con- sisting 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 com- parison of the clustering quality and computation time of the algorithms.

» Show BibTeX

title={An evaluation of two automatic landmark building discovery algorithms for city reconstruction},
author={Weyand, Tobias and Hosang, Jan and Leibe, Bastian},
booktitle={ECCV Workshop},

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