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Cited by 9,201

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

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

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Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, Sabine·2012·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2012.120
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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.

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Research works printed but not linked: 29

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Metadata

Authors
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2012-11
Volume
34
Issue
11
Pages
2274-2282
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2012.120
OpenAlex
W2118246710
Cited by
9,201
References
1
FWCI
356.70
Type
journal article
Language
en
Open access
green
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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