稀疏分解和图拉普拉斯正则化的图像前景背景分割方法

doi:10.3785/j.issn.1008-973X.2024.05.011

浙江大学学报(工学版)

2024, Vol. 58

Issue (5): 979-987 DOI: 10.3785/j.issn.1008-973X.2024.05.011

计算机技术、通信技术

稀疏分解和图拉普拉斯正则化的图像前景背景分割方法

谭婷芳1(

),蔡万源1,蒋俊正1,2,3,*(

)

1. 桂林电子科技大学信息与通信学院，广西壮族自治区桂林 541004
2. 桂林电子科技大学卫星导航定位与位置服务国家地方联合工程研究中心，广西壮族自治区桂林 541004
3. 西安电子科技大学杭州研究院，浙江杭州 311231

Image foreground-background segmentation method based on sparse decomposition and graph Laplacian regularization

Tingfang TAN1(

),Wanyuan CAI1,Junzheng JIANG1,2,3,*(

)

1. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2. State and Local Joint Engineering Research Center for Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin 541004, China
3. Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China

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摘要：

针对现有图像前景背景分割方法的分割结果存在孤立像素点的问题，利用图信号处理理论和稀疏分解模型，提出新的图像前景背景分割方法. 将图像的内在结构建模为图，通过图模型有效地刻画像素之间的内在关联性. 将图像的像素强度建模为图信号，其中图像背景作为平滑分量，由一组图傅里叶变换基函数线性表示，叠加在背景上的前景为稀疏分量，前景像素间的连通性可由图拉普拉斯正则化项进行刻画. 将图像前景背景分割问题归结为包含稀疏分解模型和图拉普拉斯正则化项的约束优化问题，采用交替方向乘子法对该优化问题进行求解. 实验结果表明，与现有的其他方法相比，所提方法具有更好的分割效果.

关键词： 图信号处理; 图拉普拉斯正则化; 图傅里叶变换基函数; 稀疏分解; 前景背景分割

Abstract:

A new method for segmenting the foreground and background of images was proposed by using the graph signal processing theory and sparse decomposition model aiming at the problem of isolated pixel points in the segmentation results of existing image foreground-background segmentation methods. The intrinsic structure of an image was modeled as a graph, and the intrinsic correlation between pixels was effectively characterized by the graph model. The pixel intensity of the image was modeled as a graph signal. The image background was linearly represented as a smooth component by a set of graph Fourier transform basis functions, the foreground overlaid on the background was a sparse component, and the connectivity between foreground pixels could be characterized by the graph Laplacian regularization term. The image foreground-background segmentation problem was reduced to a constrained optimization problem incorporating the sparse decomposition model and graph Laplacian regularization term, and the alternating direction multiplier method was adopted to solve the optimization problem. The experimental results show that the proposed method has better segmentation performance compared with other existing methods.

Key words: graph signal processing graph Laplacian regularization graph Fourier transform basis function sparse decomposition foreground-background segmentation

收稿日期: 2023-07-03 出版日期: 2024-04-26

CLC:

TN 911

基金资助: 国家自然科学基金资助项目（62171146, 62261014）；广西创新驱动发展专项资助项目（桂科AA21077008）；广西自然科学杰出青年基金资助项目（2021GXNSFFA220004）；广西科技基地和人才专项资助项目（桂科AD21220112）；桂林电子科技大学研究生教育创新计划资助项目（2022YCXS039）.

通讯作者: 蒋俊正 E-mail: 21022303141@mails.guet.edu.cn;jzjiang@guet.edu.cn

作者简介: 谭婷芳（1997—），女，硕士生，从事图信号处理理论与应用研究. orcid.org/0000-0001-6882-6317. E-mail：21022303141@mails.guet.edu.cn

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引用本文:

谭婷芳,蔡万源,蒋俊正. 稀疏分解和图拉普拉斯正则化的图像前景背景分割方法[J]. 浙江大学学报(工学版), 2024, 58(5): 979-987.

Tingfang TAN,Wanyuan CAI,Junzheng JIANG. Image foreground-background segmentation method based on sparse decomposition and graph Laplacian regularization. Journal of ZheJiang University (Engineering Science), 2024, 58(5): 979-987.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.05.011 或 https://www.zjujournals.com/eng/CN/Y2024/V58/I5/979

图 1 采用不同方法得到的合成图像分割结果对比

图 2 不同方法在MSRA数据集上的分割结果对比

图 3 采用不同方法得到的屏幕内容图像分割结果对比

表 1 不同方法在合成图像上的分割性能对比

表 2 不同方法在MSRA数据集上的分割性能对比

图 4 参数τ的灵敏度分析

图 5 参数γ的灵敏度分析

图 6 不同基函数数量下提出方法的分割结果

图 7 不同图像块大小下提出方法的分割结果

图 8 F1与迭代次数的关系

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