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

被引：0

作者：

Tan T. ^{[1
]}

Cai W. ^{[1
]}

Jiang J. ^{[1
,2
,3
]}

机构：

[1] School of Information and Communication, Guilin University of Electronic Technology, Guilin

[2] State and Local Joint Engineering Research Center for Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin

[3] Hangzhou Institute of Technology, Xidian University, Hangzhou

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2024年 / 58卷 / 05期

关键词：

foreground-background segmentation; graph Fourier transform basis function; graph Laplacian regularization; graph signal processing; sparse decomposition;

D O I：

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

中图分类号：

学科分类号：

摘要：

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. © 2024 Zhejiang University. All rights reserved.

引用

页码：979 / 987

页数：8

共 34 条

[1]

MUKHERJEE D, CHRYSAFIS C, SAID A., JPEG2000-matched MRC compression of compound documents, Proceedings. International Conference on Image Processing, (2002)

[2]

WANG G, LI W, ZULUAGA M A, Et al., Interactive medical image segmentation using deep learning with image-specific fine tuning [J], IEEE Transactions on Medical Imaging, 37, 7, pp. 1562-1573, (2018)

[3]

YIN X C, ZUO Z Y, TIAN S, Et al., Text detection, tracking and recognition in video: a comprehensive survey [J], IEEE Transactions on Image Processing, 25, 6, pp. 2752-2773, (2016)

[4]

LIN T, HAO P., Compound image compression for real-time computer screen image transmission [J], IEEE Transactions on Image Processing, 14, 8, pp. 993-1005, (2005)

[5]

BOTTOU L, HAFFNER P, HOWARD P G, Et al., High quality document image compression with "DjVu" [J], Journal of Electronic Imaging, 7, 3, pp. 410-425, (1998)

[6]

MINAEE S, WANG Y., Screen content image segmentation using least absolute deviation fitting, IEEE International Conference on Image Processing, pp. 3295-3299, (2015)

[7]

MINAEE S, WANG Y., Screen content image segmentation using sparse decomposition and total variation minimization, IEEE International Conference on Image Processing, pp. 3882-3886, (2016)

[8]

MINAEE S, WANG Y., An ADMM approach to masked signal decomposition using subspace representation [J], IEEE Transactions on Image Processing, 28, 7, pp. 3192-3204, (2019)

[9]

HU W, PANG J, LIU X, Et al., Graph signal processing for geometric data and beyond: theory and applications [J], IEEE Transactions on Multimedia, 24, pp. 3961-3977, (2021)

[10]

ORTEGA A, FROSSARD P, KOVACEVIC J, Et al., Graph signal processing: overview, challenges, and applications [J], Proceedings of the IEEE, 106, 5, pp. 808-828, (2018)

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