SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

SLIC Superpixels

Superpixels are becoming increasingly popular for use in computer vision applications. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. We introduce a novel algorithm that clusters pixels in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels. The si...

BSLIC: SLIC Superpixels Based on Boundary Term

A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to imp...

Improving the Speed of Multiresolution Segmentation Using Slic Superpixels

The popular multiresolution segmentation (MRS) algorithm is time and memory consuming when dealing with large images because it uses the pixel-grid for the initial object representation. In this study, we have tested a new workflow for image segmentation of remote sensing data, starting the MRS (using the ESP2 tool) from the superpixel level (using SLIC superpixels) and aiming at dramatically r...

Shape Based Outlier Detection in SLIC Superpixels

gSLICr: SLIC superpixels at over 250Hz

We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.