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

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

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

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

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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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

Received: 03 July 2023 Published: 26 April 2024

CLC:

TN 911

Fund: 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）.

Corresponding Authors: Junzheng JIANG E-mail: 21022303141@mails.guet.edu.cn;jzjiang@guet.edu.cn

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Cite this article:

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.

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

稀疏分解和图拉普拉斯正则化的图像前景背景分割方法

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

Fig.1 Comparison of synthetic image segmentation results by using different methods

Fig.2 Comparison of segmentation results of different methods on MSRA dataset

Fig.3 Comparison of screen content image segmentation results by using different methods

Tab.1 Comparison of segmentation performance of different methods on synthetic image %

Tab.2 Comparison of segmentation performance of different methods on MSRA dataset %

Fig.4 Sensitivity analysis of parameter τ

Fig.5 Sensitivity analysis of parameter γ

Fig.6 Segmentation results of proposed method with different number of basis functions

Fig.7 Segmentation results of proposed method with different image block sizes

Fig.8 F1 value versus number iterations


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