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.
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Weyand, T., Hosang, J., Leibe, B. (2012). An Evaluation of Two Automatic Landmark Building Discovery Algorithms for City Reconstruction. In: Kutulakos, K.N. (eds) Trends and Topics in Computer Vision. ECCV 2010. Lecture Notes in Computer Science, vol 6554. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35740-4_24
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DOI: https://doi.org/10.1007/978-3-642-35740-4_24
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