SMURFS: superpixels from multi-scale refinement of super-regions

Luengo, Imanol, Basham, Mark and French, Andrew P. (2016) SMURFS: superpixels from multi-scale refinement of super-regions. In: British Machine Vision Conference (BMVC 2016), 20-22nd Sept 2016, York, UK.

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Recent applications in computer vision have come to rely on superpixel segmentation as a pre-processing step for higher level vision tasks, such as object recognition, scene labelling or image segmentation. Here, we present a new algorithm, Superpixels from MUlti-scale ReFinement of Super-regions (SMURFS), which not only obtains state-of-the-art superpixels, but can also be applied hierarchically to form what we call n-th order super-regions. In essence, starting from a uniformly distributed set of super-regions, the algorithm iteratively alternates graph-based split and merge optimization schemes which yield superpixels that better represent the image. The split step is performed over the pixel grid to separate large super-regions into different smaller superpixels. The merging process, conversely, is performed over the superpixel graph to create 2nd-order super-regions (super-segments). Iterative refinement over two scale of regions allows the algorithm to achieve better over-segmentation results than current state-of-the-art methods, as experimental results show on the public Berkeley Segmentation Dataset (BSD500).

Item Type: Conference or Workshop Item (Paper)
Keywords: Segmentation, Super pixels
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: French, Andrew
Date Deposited: 21 Sep 2016 10:44
Last Modified: 04 May 2020 18:11

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