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SConE: Siamese Constellation Embedding Descriptor for Image Matching

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11129)

Abstract

Numerous computer vision applications rely on local feature descriptors, such as SIFT, SURF or FREAK, for image matching. Although their local character makes image matching processes more robust to occlusions, it often leads to geometrically inconsistent keypoint matches that need to be filtered out, e.g. using RANSAC. In this paper we propose a novel, more discriminative, descriptor that includes not only local feature representation, but also information about the geometric layout of neighbouring keypoints. To that end, we use a Siamese architecture that learns a low-dimensional feature embedding of keypoint constellation by maximizing the distances between non-corresponding pairs of matched image patches, while minimizing it for correct matches. The 48-dimensional floating point descriptor that we train is built on top of the state-of-the-art FREAK descriptor achieves significant performance improvement over the competitors on a challenging TUM dataset.

Keywords

  • Feature descriptor
  • Image matching
  • Siamese networks

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Acknowledgement

This research was supported by Google Sponsor Research Agreement under the project “Efficient visual localization on mobile devices”.

The Titan X Pascal used for this research was donated by the NVIDIA Corporation.

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Correspondence to Jacek Komorowski .

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Trzcinski, T., Komorowski, J., Dabala, L., Czarnota, K., Kurzejamski, G., Lynen, S. (2019). SConE: Siamese Constellation Embedding Descriptor for Image Matching. In: Leal-Taixé, L., Roth, S. (eds) Computer Vision – ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science(), vol 11129. Springer, Cham. https://doi.org/10.1007/978-3-030-11009-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-11009-3_24

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