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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Nov. 2012 (vol. 34 no. 11)
pp. 2274-2282
| ASCII Text | x | ||
| R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, Sabine Süsstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov., 2012. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2012.120, author = {R. Achanta and A. Shaji and K. Smith and A. Lucchi and P. Fua and Sabine Süsstrunk}, title = {SLIC Superpixels Compared to State-of-the-Art Superpixel Methods}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {34}, number = {11}, issn = {0162-8828}, year = {2012}, pages = {2274-2282}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.120}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - SLIC Superpixels Compared to State-of-the-Art Superpixel Methods IS - 11 SN - 0162-8828 SP2274 EP2282 EPD - 2274-2282 A1 - R. Achanta, A1 - A. Shaji, A1 - K. Smith, A1 - A. Lucchi, A1 - P. Fua, A1 - Sabine Süsstrunk, PY - 2012 KW - pattern clustering KW - computer vision KW - image segmentation KW - iterative methods KW - supervoxel generation KW - SLIC superpixels KW - computer vision KW - image boundary KW - memory efficiency KW - segmentation performance KW - simple linear iterative clustering KW - k-means clustering approach KW - superpixel generation KW - Clustering algorithms KW - Image segmentation KW - Complexity theory KW - Image color analysis KW - Image edge detection KW - Measurement uncertainty KW - Approximation algorithms KW - k-means KW - Superpixels KW - segmentation KW - clustering VL - 34 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
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:
pattern clustering,computer vision,image segmentation,iterative methods,supervoxel generation,SLIC superpixels,computer vision,image boundary,memory efficiency,segmentation performance,simple linear iterative clustering,k-means clustering approach,superpixel generation,Clustering algorithms,Image segmentation,Complexity theory,Image color analysis,Image edge detection,Measurement uncertainty,Approximation algorithms,k-means,Superpixels,segmentation,clustering
Citation:
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, Sabine Süsstrunk, "SLIC Superpixels Compared to State-of-the-Art Superpixel Methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov. 2012, doi:10.1109/TPAMI.2012.120
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