Matching street-level images to a database of airborne images is hard because of extreme viewpoint and illumination differences. Color/gradient distributions or local descriptors fail to match forcing us to rely on the structure of self-similarity of patterns on facades. We propose to capture this structure with a novel "scale-selective self-similarity" (S 4) descriptor which is computed at each point on the facade at its inherent scale. To achieve this, we introduce a new method for scale selection which enables the extraction and segmentation of facades as well. Matching is done with a Bayesian classification of the street-view query S4 descriptors given all labeled descriptors in the bird's-eye-view database. We show experimental results on retrieval accuracy on a challenging set of publicly available imagery and compare with standard SIFT-based techniques. © 2012 Springer-Verlag.
CITATION STYLE
Bansal, M., Daniilidis, K., & Sawhney, H. (2012). Ultra-wide baseline facade matching for geo-localization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7583 LNCS, pp. 175–186). Springer Verlag. https://doi.org/10.1007/978-3-642-33863-2_18
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