  We consider the problem of segmenting an image into superpixels in the
context of $k$-means clustering, in which we wish to decompose an image into
local, homogeneous regions corresponding to the underlying objects. Our novel
approach builds upon the widely used Simple Linear Iterative Clustering (SLIC),
and incorporate a measure of objects' structure based on the spectral residual
of an image. Based on this combination, we propose a modified initialisation
scheme and search metric, which helps keeps fine-details. This combination
leads to better adherence to object boundaries, while preventing unnecessary
segmentation of large, uniform areas, while remaining computationally tractable
in comparison to other methods. We demonstrate through numerical and visual
experiments that our approach outperforms the state-of-the-art techniques.
