@ARTICLE{6205760,
author={Achanta, R. and Shaji, A. and Smith, K. and Lucchi, A. and Fua, P. and Süsstrunk, S.},
journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on},
title={SLIC Superpixels Compared to State-of-the-Art Superpixel Methods},
year={2012},
volume={34},
number={11},
pages={2274-2282},
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.},
keywords={computer vision;image segmentation;iterative methods;pattern clustering;SLIC superpixels;computer vision;image boundary;k-means clustering approach;memory efficiency;segmentation performance;simple linear iterative clustering;superpixel generation;supervoxel generation;Approximation algorithms;Clustering algorithms;Complexity theory;Image color analysis;Image edge detection;Image segmentation;Measurement uncertainty;Superpixels;clustering;k-means;segmentation;Algorithms;Image Enhancement;Image Interpretation, Computer-Assisted;Pattern Recognition, Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer-Assisted},
doi={10.1109/TPAMI.2012.120},
ISSN={0162-8828},
month={Nov},}