Texture-Aware Superpixel Segmentation

• URL: http://arxiv.org/abs/1901.11111v4
• Date: Wed, 17 Sep 2025 13:34:59 GMT
• Title: Texture-Aware Superpixel Segmentation
• Authors: Remi Giraud, Vinh-Thong Ta, Nicolas Papadakis, Yannick Berthoumieu,
• Abstract summary: We develop a Texture-Aware SuperPixel (TASP) method to segment smooth and textured areas.<n>To ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed.<n>TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.
• Score: 5.027313829438866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.

Related papers

• Learning to Make Keypoints Sub-Pixel Accurate [80.55676599677824]
  This work addresses the challenge of sub-pixel accuracy in detecting 2D local features. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features.
  arXiv  Detail & Related papers  (2024-07-16T12:39:56Z)
• Adaptive Superpixel for Active Learning in Semantic Segmentation [34.0733215363568]
  We propose a superpixel-based active learning framework, which collects a dominant label per superpixel instead of pixel-wise annotations. Obtaining a dominant label per superpixel drastically reduces annotators' burden as it requires fewer clicks. We also devise a sieving mechanism that identifies and excludes potentially noisy annotations from learning.
  arXiv  Detail & Related papers  (2023-03-29T16:07:06Z)
• 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)
• Saliency Enhancement using Superpixel Similarity [77.34726150561087]
  Saliency Object Detection (SOD) has several applications in image analysis. Deep-learning-based SOD methods are among the most effective, but they may miss foreground parts with similar colors. We introduce a post-processing method, named textitSaliency Enhancement over Superpixel Similarity (SESS) We demonstrate that SESS can consistently and considerably improve the results of three deep-learning-based SOD methods on five image datasets.
  arXiv  Detail & Related papers  (2021-12-01T17:22:54Z)
• Generating Superpixels for High-resolution Images with Decoupled Patch Calibration [82.21559299694555]
  Patch Networks (PCNet) is designed to efficiently and accurately implement high-resolution superpixel segmentation. DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries.
  arXiv  Detail & Related papers  (2021-08-19T10:33:05Z)
• 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)
• Superpixel Segmentation Based on Spatially Constrained Subspace Clustering [57.76302397774641]
  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.
  arXiv  Detail & Related papers  (2020-12-11T06:18:36Z)
• SegFix: Model-Agnostic Boundary Refinement for Segmentation [75.58050758615316]
  We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by the empirical observation that the label predictions of interior pixels are more reliable, we propose to replace the originally unreliable predictions of boundary pixels by the predictions of interior pixels.
  arXiv  Detail & Related papers  (2020-07-08T17:08:08Z)
• Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching [2.84279467589473]
  We propose a new Nearest Neighbor-based Superpixel Clustering (NNSC) method to generate texture-aware superpixels in a limited computational time.
  arXiv  Detail & Related papers  (2020-03-09T21:11:21Z)
• Robust superpixels using color and contour features along linear path [5.746869663956391]
  We propose a framework that provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP)<n>A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel.<n>SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods.
  arXiv  Detail & Related papers  (2019-03-17T23:00:13Z)
• SCALP: Superpixels with Contour Adherence using Linear Path [5.746869663956391]
  We propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework.<n>The proposed framework produces compact and superpixels that adhere to contour contours.
  arXiv  Detail & Related papers  (2019-03-17T19:23:00Z)

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.