Dynamic spectral residual superpixels

Jianchao ZHANG, Angelica I. AVILES-RIVERO*, Daniel HEYDECKER, Xiaosheng ZHUANG, Raymond CHAN, Carola Bibiane SCHÖNLIEB

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)


We consider the problem of segmenting an image into superpixels in the context of k-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects. Our novel approach builds upon the widely used Simple Linear Iterative Clustering (SLIC), and incorporate a measure of objects’ structure based on the spectral residual of an image. Based on this combination, we propose a modified initialisation scheme and search metric, which keeps fine-details. This combination leads to better adherence to object boundaries, while preventing unnecessary segmentation of large, uniform areas, and remaining computationally tractable in comparison to other methods. We demonstrate through numerical and visual experiments that our approach outperforms the state-of-the-art techniques.

Original languageEnglish
Article number107705
Number of pages13
JournalPattern Recognition
Early online date21 Oct 2020
Publication statusPublished - Apr 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd


  • K-means
  • Segmentation
  • Spectral residual
  • Superpixels


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