Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

TL;DR

This paper introduces a novel superpixel segmentation method based on spatially constrained subspace clustering, improving boundary preservation and segmentation accuracy by incorporating spatial regularization and content-aware clustering.

Contribution

It formulates superpixel segmentation as a subspace clustering problem with spatial regularization, addressing boundary detail preservation and boundary confusion issues in industrial applications.

Findings

Achieves superior segmentation quality compared to state-of-the-art methods.

Effectively preserves detailed content boundaries in superpixels.

Demonstrates robustness across multiple standard datasets.

Abstract

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.