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Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence (ISSN: 0162-8828), vol. 34, num. 11, p. 2274 - 2282 Los Alamitos: Institute of Electrical and Electronics Engineers, 2012

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: Superpixels ; Segmentation ; Clustering ; k-means ; NCCR-MICS/EMSP ; NCCR-MICS Note: A previous version of this article was published as a EPFL Technical Report in 2010: Supplementary material can be found at: Reference EPFL-ARTICLE-177415doi:10.1109/TPAMI.2012.120View record in Web of Science

Autor: Achanta, Radhakrishna; Shaji, Appu; Smith, Kevin; Lucchi, Aurélien; Fua, Pascal; Süsstrunk, Sabine


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