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Image Descriptors and Similarity Measures

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Computer Vision

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Definition

An image descriptor is a vector representing concisely the content of an image. A similarity measure is a function estimating the similarity between two objects, usually represented by vectors.

Background

Image descriptors are an ubiquitous tool in computer vision. By representing the content of an image or an image region in a compact and robust way, they make matching problems more efficient, as shown in Figs. 1 and 2. Typically, a descriptor is used to query a set of descriptors, looking for the most similar descriptor in the set. Efficient algorithms, such as hashing, can be used to make this search extremely fast, even for large databases, when the Euclidean distance can be used as similarity measure. Applications range from simultaneous localization and mapping (SLAM) and Structure from Motion (SfM) to image retrieval and object recognition.

Fig. 1
figure 1

Affine regions represented as ellipses matched across two images using their local descriptors. (Images from [1])

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Author information

Authors and Affiliations

  1. ENPC ParisTech, Champs-sur-Marne, France

    Vincent Lepetit

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  1. Vincent Lepetit
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Corresponding author

Correspondence to Vincent Lepetit .

Section Editor information

  1. Microsoft Research, Redmond, WA, USA

    Sing Bing Kang

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Lepetit, V. (2020). Image Descriptors and Similarity Measures. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_797-1

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  • DOI: https://doi.org/10.1007/978-3-030-03243-2_797-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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