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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2017.520