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SIFTpack: A Compact Representation for Efficient SIFT Matching

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Book cover Dense Image Correspondences for Computer Vision

Abstract

Computing distances between large sets of SIFT descriptors is a basic step in numerous algorithms in computer vision. When the number of descriptors is large, as is often the case, computing these distances can be extremely time consuming. We propose the SIFTpack: a compact way of storing SIFT descriptors, which enables significantly faster calculations between sets of SIFTs than the current solutions. SIFTpack can be used to represent SIFTs densely extracted from a single image or sparsely from multiple different images. We show that the SIFTpack representation saves both storage space and run time, for both finding nearest neighbors and computing all distances between all descriptors. The usefulness of SIFTpack is demonstrated as an alternative implementation for K-means dictionaries of visual words and for image retrieval.

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Acknowledgements

This research was supported in part by the Ollendorf Foundation and by the Israel Ministry of Science. We would also like to thank Prof. Michael Elad for useful conversations and good ideas.

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Authors and Affiliations

  1. Technion Israel Institute of Technology, Haifa, 32000, Israel

    Alexandra Gilinsky & Lihi Zelnik-Manor

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  1. Alexandra Gilinsky
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  2. Lihi Zelnik-Manor
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Correspondence to Lihi Zelnik-Manor .

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Editors and Affiliations

  1. The Open University of Israel, Raanana, Israel

    Tal Hassner

  2. Google Research, Cambridge, Massachusetts, USA

    Ce Liu

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Gilinsky, A., Zelnik-Manor, L. (2016). SIFTpack: A Compact Representation for Efficient SIFT Matching. In: Hassner, T., Liu, C. (eds) Dense Image Correspondences for Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-319-23048-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-23048-1_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23047-4

  • Online ISBN: 978-3-319-23048-1

  • eBook Packages: EngineeringEngineering (R0)

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