Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

• URL: http://arxiv.org/abs/2012.06149v1
• Date: Fri, 11 Dec 2020 06:18:36 GMT
• Title: Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
• Authors: Hua Li, Yuheng Jia, Runmin Cong, Wenhui Wu, Sam Kwong, and Chuanbo Chen
• Abstract summary: We consider each representative region with independent semantic information as a subspace, and formulate superpixel segmentation as a subspace clustering problem. 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. We 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.
• Score: 57.76302397774641
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• QMambaBSR: Burst Image Super-Resolution with Query State Space Model [55.56075874424194]
  Burst super-resolution aims to reconstruct high-resolution images with higher quality and richer details by fusing the sub-pixel information from multiple burst low-resolution frames. In BusrtSR, the key challenge lies in extracting the base frame's content complementary sub-pixel details while simultaneously suppressing high-frequency noise disturbance. We introduce a novel Query Mamba Burst Super-Resolution (QMambaBSR) network, which incorporates a Query State Space Model (QSSM) and Adaptive Up-sampling module (AdaUp)
  arXiv  Detail & Related papers  (2024-08-16T11:15:29Z)
• Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images [64.72242126879503]
  Hyperspectral images (HSI) clustering is an important but challenging task. We first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI. We then design a superpixel graph contrastive clustering model to learn discriminative superpixel representations.
  arXiv  Detail & Related papers  (2024-03-04T07:40:55Z)
• Learning Invariant Inter-pixel Correlations for Superpixel Generation [12.605604620139497]
  Learnable features exhibit constrained discriminative capability, resulting in unsatisfactory pixel grouping performance. We propose the Content Disentangle Superpixel algorithm to selectively separate the invariant inter-pixel correlations and statistical properties. The experimental results on four benchmark datasets demonstrate the superiority of our approach to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-28T09:46:56Z)
• Pixel-Level Clustering Network for Unsupervised Image Segmentation [3.69853388955692]
  We present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. We also propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images.
  arXiv  Detail & Related papers  (2023-10-24T23:06:29Z)
• Unsupervised Superpixel Generation using Edge-Sparse Embedding [18.92698251515116]
  partitioning an image into superpixels based on the similarity of pixels with respect to features can significantly reduce data complexity and improve subsequent image processing tasks. We propose a non-convolutional image decoder to reduce the expected number of contrasts and enforce smooth, connected edges in the reconstructed image. We generate edge-sparse pixel embeddings by encoding additional spatial information into the piece-wise smooth activation maps from the decoder's last hidden layer and use a standard clustering algorithm to extract high quality superpixels.
  arXiv  Detail & Related papers  (2022-11-28T15:55:05Z)
• Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion [67.35540259040806]
  We propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DCNet. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components. We append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product.
  arXiv  Detail & Related papers  (2022-05-07T23:40:36Z)
• Hyperbolic Image Segmentation [16.50554261593531]
  We propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image opens up new possibilities and practical benefits for segmentation.
  arXiv  Detail & Related papers  (2022-03-11T13:07:51Z)
• Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification [49.32130776974202]
  SP-DLRR is composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-25T10:47:26Z)
• AINet: Association Implantation for Superpixel Segmentation [82.21559299694555]
  We propose a novel textbfAssociation textbfImplantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids. Our method could not only achieve state-of-the-art performance but maintain satisfactory inference efficiency.
  arXiv  Detail & Related papers  (2021-01-26T10:40:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.