Title: Similarity and symmetry measures based on fuzzy descriptors of image objects’ composition
Authors: Iwanowski, Marcin
Grzabka, Marcin
Citation: WSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 299-308.
Issue Date: 2021
Publisher: Václav Skala - UNION Agency
Document type: conferenceObject
konferenční příspěvek
URI: http://hdl.handle.net/11025/45036
ISBN: 978-80-86943-34-3
ISSN: 2464-4617
2464–4625(CD/DVD)
Keywords: podobnost obrazu;detekce symetrie;detekce objektů;fuzzy logika
Keywords in different language: image similarity;symmetry detection;object detection;fuzzy logic
Abstract in different language: The paper describes a method for measuring the similarity and symmetry of an image annotated with boundingboxes indicating image objects. The latter representation became popular recently due to the rapid development offast and efficient deep-learning-based object-detection methods. The proposed approach allows for comparing setsof bounding boxes to estimate the degree of similarity of their underlying images. It is based on the fuzzy approachthat uses the fuzzy mutual position (FMP) matrix to describe spatial composition and relations between boundingboxes within an image. A method of computing the similarity of two images described by their FMP matrices isproposed and the algorithm of its computation. It outputs the single scalar value describing the degree of content-based image similarity. By modifying the method’s parameters, instead of similarity, the reflectional symmetry ofobject composition may also be measured. The proposed approach allows for measuring differences in objects’composition of various intensities. It is also invariant to translation and scaling and – in case of symmetry detection– position and orientation of the symmetry axis. A couple of examples illustrate the method.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG 2021: Full Papers Proceedings

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