DMCA
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
| Venue: | PAMI |
| Citations: | 222 - 3 self |
BibTeX
@ARTICLE{Achanta_slicsuperpixels,
author = {Radhakrishna Achanta and Kevin Smith and Aurelien Lucchi and Pascal Fua},
title = {SLIC Superpixels Compared to State-of-the-Art Superpixel Methods},
journal = {PAMI},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract—Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation. Index Terms—Superpixels, segmentation, clustering, k-means. I.
Keyphrases
state-of-the-art superpixel method slic superpixels compared segmentation performance memory efficient index term superpixels simple linear iterative clustering recent year image boundary memory efficiency state-of-the-art superpixel algorithm abstract computer vision application good superpixel algorithm previous method new superpixel algorithm k-means clustering approach
