Bilateral K-Means for Superpixel Computation (the SLIC Method)

Robin Gay (1) , Jérémie Lecoutre (1) , Nicolas Menouret (1) , Arthur Morillon (1) , Pascal Monasse (2)

1 ENPC - École des Ponts ParisTech
2 LIGM - Laboratoire d'Informatique Gaspard-Monge

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 postprocessing that ensures that superpixels are connected, a property not ensured by the original method. Source Code The commented C++ source code for SLIC and its documentation are available on the web page of this article 1. Usage instructions are detailed in the README.md file of the archive.

Mots clés: superpixel; image segmentation; SLIC (Simple Linear Iterative Clustering)

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Robin Gay, Jérémie Lecoutre, Nicolas Menouret, Arthur Morillon, Pascal Monasse. Bilateral K-Means for Superpixel Computation (the SLIC Method). Image Processing On Line, 2022, ⟨10.5201/ipol.2022.373⟩. ⟨hal-03651336⟩