Study of sift descriptors for image matching based localization in urban street view context

David Picard 1 Matthieu Cord 1 Eduardo Valle
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this paper we evaluate the quality of vote-based retrieval using SIFT descriptors in a database of street view photography, a challeging context where the fraction of mismatched descriptors tends to be very high. This work is part of the iTowns project, for which high resolution street views of Paris have been taken. The goal is to retrieve the views of a urban scene given a query picture. We have carried out experiments for several techniques of image matching, including a post-processing step to check the geometric consistency of the results. We have shown that the efficiency of SIFT based matching depends largely on the image database content, and that the post-processing step is essential to the retrieval performances.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01298815
Contributor : Lip6 Publications <>
Submitted on : Wednesday, April 6, 2016 - 4:30:52 PM
Last modification on : Wednesday, October 2, 2019 - 8:14:01 AM

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  • HAL Id : hal-01298815, version 1

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David Picard, Matthieu Cord, Eduardo Valle. Study of sift descriptors for image matching based localization in urban street view context. ISPRS workshop CMRT'09. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Sep 2009, Paris, France. pp.193-198. ⟨hal-01298815⟩

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