Superpixels and Polygons Using Simple Non-iterative Clustering

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 als...

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Vydáno v:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 4895 - 4904
Hlavní autoři: Achanta, Radhakrishna, Susstrunk, Sabine
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2017
Témata:
Clustering algorithms
Estimation
Image color analysis
Image segmentation
Iterative algorithms
Memory management
Partitioning algorithms
ISSN:1063-6919, 1063-6919
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Abstract 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.
AbstractList 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.
Author Achanta, Radhakrishna
Susstrunk, Sabine
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  fullname: Achanta, Radhakrishna
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  givenname: Sabine
  surname: Susstrunk
  fullname: Susstrunk, Sabine
  email: sabine.susstrunk@epfl.ch
  organization: Sch. of Comput. & Commun. Sci., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
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Snippet We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Unlike SLIC, our algorithm is non-iterative, enforces...
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StartPage 4895
SubjectTerms Clustering algorithms
Estimation
Image color analysis
Image segmentation
Iterative algorithms
Memory management
Partitioning algorithms
Title Superpixels and Polygons Using Simple Non-iterative Clustering
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