  Superpixel segmentation aims at dividing the input image into some
representative regions containing pixels with similar and consistent intrinsic
properties, without any prior knowledge about the shape and size of each
superpixel. In this paper, to alleviate the limitation of superpixel
segmentation applied in practical industrial tasks that detailed boundaries are
difficult to be kept, we regard each representative region with independent
semantic information as a subspace, and correspondingly formulate superpixel
segmentation as a subspace clustering problem to preserve more detailed content
boundaries. We show that a simple integration of superpixel segmentation with
the conventional subspace clustering does not effectively work due to the
spatial correlation of the pixels within a superpixel, which may lead to
boundary confusion and segmentation error when the correlation is ignored.
Consequently, we devise a spatial regularization and propose a novel convex
locality-constrained subspace clustering model that is able to constrain the
spatial adjacent pixels with similar attributes to be clustered into a
superpixel and generate the content-aware superpixels with more detailed
boundaries. Finally, the proposed model is solved by an efficient alternating
direction method of multipliers (ADMM) solver. Experiments on different
standard datasets demonstrate that the proposed method achieves superior
performance both quantitatively and qualitatively compared with some
state-of-the-art methods.
