Image segmentation with adaptive sparse grids

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

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

Abstract

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
Pages160-165
Number of pages6
DOIs
StatePublished - Dec 1 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

Other

Other26th Australasian Joint Conference on Artificial Intelligence, AI 2013
CountryNetherlands
CityDunedin
Period12/1/1312/6/13

Fingerprint

Sparse Grids
Adaptive Grid
Image segmentation
Image Segmentation
Spectral Clustering
Pixels
Pixel
Segmentation
Eigenvalue
Evaluate

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Peherstorfer, B., Adorf, J., Pflüger, D., & Bungartz, H. J. (2013). Image segmentation with adaptive sparse grids. In AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings (pp. 160-165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8272 LNAI). https://doi.org/10.1007/978-3-319-03680-9_17

Image segmentation with adaptive sparse grids. / Peherstorfer, Benjamin; Adorf, Julius; Pflüger, Dirk; Bungartz, Hans Joachim.

AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. 2013. p. 160-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8272 LNAI).

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

Peherstorfer, B, Adorf, J, Pflüger, D & Bungartz, HJ 2013, Image segmentation with adaptive sparse grids. in AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8272 LNAI, pp. 160-165, 26th Australasian Joint Conference on Artificial Intelligence, AI 2013, Dunedin, Netherlands, 12/1/13. https://doi.org/10.1007/978-3-319-03680-9_17
Peherstorfer B, Adorf J, Pflüger D, Bungartz HJ. Image segmentation with adaptive sparse grids. In AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. 2013. p. 160-165. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-03680-9_17
Peherstorfer, Benjamin ; Adorf, Julius ; Pflüger, Dirk ; Bungartz, Hans Joachim. / Image segmentation with adaptive sparse grids. AI 2013: Advances in Artificial Intelligence - 26th Australasian Joint Conference, Proceedings. 2013. pp. 160-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{45d68c9dc40f4192910bf194050153f2,
title = "Image segmentation with adaptive sparse grids",
abstract = "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.",
keywords = "Image segmentation, Out-of-sample extension, Sparse grids",
author = "Benjamin Peherstorfer and Julius Adorf and Dirk Pfl{\"u}ger and Bungartz, {Hans Joachim}",
year = "2013",
month = "12",
day = "1",
doi = "10.1007/978-3-319-03680-9_17",
language = "English (US)",
isbn = "9783319036793",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "160--165",
booktitle = "AI 2013",

}

TY - GEN

T1 - Image segmentation with adaptive sparse grids

AU - Peherstorfer, Benjamin

AU - Adorf, Julius

AU - Pflüger, Dirk

AU - Bungartz, Hans Joachim

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

KW - Image segmentation

KW - Out-of-sample extension

KW - Sparse grids

UR - http://www.scopus.com/inward/record.url?scp=84893749999&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893749999&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-03680-9_17

DO - 10.1007/978-3-319-03680-9_17

M3 - Conference contribution

AN - SCOPUS:84893749999

SN - 9783319036793

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 160

EP - 165

BT - AI 2013

ER -