Bilateral K-Means for Superpixel Computation (the SLIC Method)
Robin Gay, Jérémie Lecoutre, Nicolas Menouret, Arthur Morillon, Pascal Monasse
published
2022-04-21
reference
Robin Gay, Jérémie Lecoutre, Nicolas Menouret, Arthur Morillon, and Pascal Monasse, Bilateral K-Means for Superpixel Computation (the SLIC Method), Image Processing On Line, 12 (2022), pp. 72–91. https://doi.org/10.5201/ipol.2022.373

Communicated by Gregory Randall
Demo edited by Pascal Monasse

Abstract

As a substitute to a full segmentation of a digital image, or as preprocessing to a segmentation algorithm, superpixels provide an over-segmentation that offers several benefits: good adherence to edges, uniformity of color inside superpixels, a richer adjacency structure than the regular grid of pixels, and the fact that each node of the graph of superpixels has a shape, which can be used in subsequent processing. Moreover, their evaluation is less subjective than a full segmentation, which somehow always involves a semantic interpretation of the scene. The SLIC method (Simple Linear Iterative Clustering) has been a very popular algorithm to compute superpixels since its introduction. Its advantage is due to its simplicity and to its computing time performance. In essence, it consists in a K-means clustering in bilateral domain, involving both position and color. We study in detail this algorithm and propose a fast, simple post-processing that ensures that superpixels are connected, a property not ensured by the original method.

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