Superpixels and Polygons Using Simple Non-iterative Clustering

By Radhakrishna Achanta, Sabine Süsstrunk. Computer Vision and Pattern Recognition, 2017

412 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4f5a498b55390655ac0bdafa06b01d69a4fd8c38

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In Computer Vision and Pattern Recognition

Citations in the corpus

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review (2020, methodology): “Other maps and products are generated on-the-ﬂy [28], [29], once any user wants to run the code [36], [37].”
Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms (2020, methodology): “In this regard, the Simple Non-Iterative Clustering (SNIC) algorithm, available in GEE, resulted very efficient in grouping similar pixels and identifying potential, individual objects [12].”