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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Nov. 2012 (vol. 34 no. 11)
pp. 2274-2282
R. Achanta, Images & Visual Representation Group, Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
A. Shaji, Images & Visual Representation Group, Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
K. Smith, Zurich Light Microscopy Center, ETH Zurich, Zurich, Switzerland
A. Lucchi, Comput. Vision Lab., Polytech. Fed. de Lausanne, Lausanne, Switzerland
P. Fua, Comput. Vision Lab., Polytech. Fed. de Lausanne, Lausanne, Switzerland
Sabine Süsstrunk, Images & Visual Representation Group, Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
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:
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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