Paper Info
Title
SLIC superpixels compared to state-of-the-art superpixel methods.
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.
Year
DOI
Venue
2012
10.1109/TPAMI.2012.120
IEEE Trans. Pattern Anal. Mach. Intell.
Keywords
Field
DocType
new superpixel algorithm,slic adheres,image boundary,memory efficiency,state-of-the-art superpixel algorithm,computer vision application,state-of-the-art superpixel methods,segmentation performance,simple linear iterative clustering,good superpixel algorithm,k-means clustering approach,clustering algorithms,clustering,k means,measurement uncertainty,image segmentation,segmentation,approximation algorithms,iterative methods,computer vision
Computer science,Pattern clustering,Image segmentation,Artificial intelligence,Cluster analysis,Computer vision,Approximation algorithm,k-means clustering,Kadir–Brady saliency detector,Pattern recognition,Iterative method,Segmentation,Machine learning
Journal
Volume
Issue
ISSN
34
11
1939-3539
Citations 
PageRank 
References 
1938
50.33
22
Authors
6
Search Limit
1001000
Name
Order
Citations
PageRank
Radhakrishna Achanta13829119.25
Appu Shaji2198055.52
Kevin Smith3243088.78
Aurelien Lucchi4241989.45
Pascal Fua512768731.45
Sabine Süsstrunk64984207.02