Superpixels and Polygons using Simple Non-Iterative Clustering

被引：358

作者：

Achanta, Radhakrishna ^{[1
]}

Susstrunk, Sabine ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, Sch Comp & Commun Sci IC, Lausanne, Switzerland

来源：

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) | 2017年

关键词：

D O I：

10.1109/CVPR.2017.520

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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-theart algorithms on quantitative benchmarks.

引用

页码：4895 / 4904

页数：10

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