Image segmentation with adaptive sparse grids

Benjamin Peherstorfer, Julius Adorf, Dirk Pflüger, Hans Joachim Bungartz

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a novel adaptive sparse grid method for unsupervised image segmentation. The method is based on spectral clustering. The use of adaptive sparse grids achieves that the dimensions of the involved eigensystem do not depend on the number of pixels. In contrast to classical spectral clustering, our sparse-grid variant is therefore able to segment larger images. We evaluate the method on real-world images from the Berkeley Segmentation Dataset. The results indicate that images with 150,000 pixels can be segmented by solving an eigenvalue system of dimensions 500 x 500 instead of 150,000 x 150,000.

Original languageEnglish (US)
Title of host publicationAI 2013
Subtitle of host publicationAdvances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings
Number of pages6
StatePublished - 2013
Event26th Australasian Joint Conference on Artificial Intelligence, AI 2013 - Dunedin, Netherlands
Duration: Dec 1 2013Dec 6 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8272 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other26th Australasian Joint Conference on Artificial Intelligence, AI 2013


  • Image segmentation
  • Out-of-sample extension
  • Sparse grids

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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