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Superpixels and Polygons Using Simple Non-Iterative Clustering
AbstractWe 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.
Related Material
[pdf][bibtex]@InProceedings{Achanta_2017_CVPR,
author = {Achanta, Radhakrishna and Susstrunk, Sabine},
title = {Superpixels and Polygons Using Simple Non-Iterative Clustering},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
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