  This paper addresses the search for a fast and meaningful image segmentation
in the context of $k$-means clustering. The proposed method builds on a
widely-used local version of Lloyd's algorithm, called Simple Linear Iterative
Clustering (SLIC). We propose an algorithm which extends SLIC to dynamically
adjust the local search, adopting superpixel resolution dynamically to
structure existent in the image, and thus provides for more meaningful
superpixels in the same linear runtime as standard SLIC. The proposed method is
evaluated against state-of-the-art techniques and improved boundary adherence
and undersegmentation error are observed, whilst still remaining among the
fastest algorithms which are tested.
