  Superpixel decomposition methods are generally used as a pre-processing step
to speed up image processing tasks. They group the pixels of an image into
homogeneous regions while trying to respect existing contours. For all
state-of-the-art superpixel decomposition methods, a trade-off is made between
1) computational time, 2) adherence to image contours and 3) regularity and
compactness of the decomposition. In this paper, we propose a fast method to
compute Superpixels with Contour Adherence using Linear Path (SCALP) in an
iterative clustering framework. The distance computed when trying to associate
a pixel to a superpixel during the clustering is enhanced by considering the
linear path to the superpixel barycenter. The proposed framework produces
regular and compact superpixels that adhere to the image contours. We provide a
detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The
obtained results outperform state-of-the-art methods in terms of standard
superpixel and contour detection metrics.
