Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

Hua LI, Yuheng JIA, Runmin CONG, Wenhui WU, Sam Tak Wu KWONG, Chuanbo CHEN

Research output: Journal PublicationsJournal Article (refereed)peer-review

6 Citations (Scopus)


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-theart methods.
Original languageEnglish
Pages (from-to)7501-7512
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number11
Early online date11 Dec 2020
Publication statusPublished - Nov 2021
Externally publishedYes

Bibliographical note

This work was supported by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101301, in part by the Natural Science Foundation of China under Grants 61772344, 62002014, in part by the Hong Kong RGC General Research Funds under 9042816 (CityU 11209819), in part by the Beijing Nova Program under Grant Z201100006820016, in part by the Fundamental Research Funds for the Central Universities under Grant 2019RC039, in part by Elite Scientist Sponsorship Program by the Beijing Association for Science and Technology, in part by Hong Kong Scholars Program, and in part by China Postdoctoral Science Foundation under Grant 2020T130050, Grant 2019M660438.


  • Locality-constrained
  • Spatial correlation
  • Subspace clustering
  • Superpixel segmentation


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