Texture Relative Superpixel Generation With Adaptive Parameters

Pan, Xiao; Zhou, Yuanfeng; Chen, Zhonggui; Zhang, Caiming

doi:10.1109/tmm.2019.2895498

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…Superpixel segmentation can be viewed as a clustering procedure on the image, the key of this problem is to estimate the assignment of each pixel to its potential clustering centers [24], [1], [26], [35], [39], [2], [36], [28], [38], [30], [17], [23]. Traditional methods model the assignment using the clustering theory [19], [1], [26], [2] or graph technique [11], [24].…”

Section: A Superpixel Segmentationmentioning

confidence: 99%

Generating Superpixels for High-resolution Images with Decoupled Patch Calibration

Wang¹,

Wei²,

Qian³

et al. 2021

Preprint

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Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. 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. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs.

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Section: A Superpixel Segmentationmentioning

confidence: 99%

Generating Superpixels for High-resolution Images with Decoupled Patch Calibration

Wang¹,

Wei²,

Qian³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Many superpixel approaches have been proposed in recent years [10][11][12] . Stutz et al [13] presented a comprehensive evaluation of 28 state-of-the-art superpixel algorithms, and some of them were designed for or can be extended to processing RGB-D images [11,[14][15][16][17][18].…”

Section: Superpixel Generation From Rgb-d Imagesmentioning

confidence: 99%

Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds

et al. 2020

Self Cite

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Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.

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“…There are two key issues on region-based segmentation methods [28], (1) how to generate the sub-regions, (2) how to realize segmentation based on sub-regions. For the first issue, the most commonly used algorithm is the Simple Linear Iterative Clustering (SLIC) [29,30] in superpixel algorithms [31,32]. The sub-regions are generated by assigning pixels to the nearest seed points, where the dissimilarity measure between pixels and seed points is modeled by combing spatial and spectral distance with scale parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

Chen

Zhao

et al. 2020

Remote Sensing

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The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

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Texture Relative Superpixel Generation With Adaptive Parameters

Cited by 10 publications

References 29 publications

Generating Superpixels for High-resolution Images with Decoupled Patch Calibration

Generating Superpixels for High-resolution Images with Decoupled Patch Calibration

Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds

Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

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