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Evolving Content-Driven Superpixels for Accurate Image Representation

  • Richard J. Lowe
  • Mark S. Nixon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

A novel approach to superpixel generation is presented that aims to reconcile image information with superpixel coverage. It is described as content-driven as the number of superpixels in any given area is dictated by the underlying image properties. By using a combination of well-established computer vision techniques, superpixels are grown and subsequently divided on detecting simple image variation. It is designed to have no direct control over the number of superpixels as this can lead to errors. The algorithm is subject to performance metrics on the Berkeley Segmentation Dataset including: explained variation; mode label analysis, as well as a measure of oversegmentation. The results show that this new algorithm can reduce the superpixel oversegmentation and retain comparable performance in all other metrics. The algorithm is shown to be stable with respect to initialisation, with little variation across performance metrics on a set of random initialisations.

Keywords

Active Contour Random Initialisation Gaussian Smoothing Local Invariant Feature Berkeley Segmentation Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Richard J. Lowe
    • 1
  • Mark S. Nixon
    • 1
  1. 1.School of Electronics and Computer ScienceUniversity of SouthamptonUK

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