SMURFS: superpixels from multi-scale refinement of super-regionsTools 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. Full text not available from this repository.
Official URL: http://www.bmva.org/bmvc/2016/papers/paper004/index.html
AbstractRecent 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).
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