Distance Between Vector-Valued Fuzzy Sets Based on Intersection Decomposition with Applications in Object Detection

  • Johan Öfverstedt
  • Nataša Sladoje
  • Joakim Lindblad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10225)

Abstract

We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single- and multi-channel distance measures between information-rich image/object representations.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johan Öfverstedt
    • 1
  • Nataša Sladoje
    • 1
    • 2
  • Joakim Lindblad
    • 1
    • 2
  1. 1.Centre for Image Analysis, Department of ITUppsala UniversityUppsalaSweden
  2. 2.Mathematical Institute of Serbian Academy of Sciences and ArtsBelgradeSerbia

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