Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Superpixels and Polygons using Simple Non-Iterative Clustering
 
conference paper

Superpixels and Polygons using Simple Non-Iterative Clustering

Achanta, Radhakrishna  
•
Süsstrunk, Sabine
2017
30Th Ieee Conference On Computer Vision And Pattern Recognition (Cvpr 2017)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the start, requires lesser memory, and is faster. Relying on the superpixel boundaries obtained using our algorithm, we also present a polygonal partitioning algorithm. We demonstrate that our superpixels as well as the polygonal partitioning are superior to the respective state-of-the-art algorithms on quantitative benchmarks.

  • Files
  • Details
  • Metrics
Type
conference paper
DOI
10.1109/Cvpr.2017.520
Web of Science ID

WOS:000418371404104

Author(s)
Achanta, Radhakrishna  
Süsstrunk, Sabine
Date Issued

2017

Publisher

Ieee

Publisher place

New York

Published in
30Th Ieee Conference On Computer Vision And Pattern Recognition (Cvpr 2017)
ISBN of the book

978-1-5386-0457-1

Total of pages

10

Series title/Series vol.

IEEE Conference on Computer Vision and Pattern Recognition

Start page

4895

End page

4904

Subjects

SLIC

•

superpixels

•

CIELAB color space

•

polygonal partitioning

URL

URL

http://ivrl.epfl.ch/research/snic_superpixels
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
IVRL  
Event nameEvent placeEvent date
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)

Honolulu, Hawaï, USA

July 21-26, 2017

Available on Infoscience
April 6, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/136422
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés