{"title":"Analysis discriminative convolutional graph dictionary learning for generalized signal classification","authors":"Yuehan Xiong, Xin Li, Wenrui Dai, Hongkai Xiong","doi":"10.1016/j.image.2025.117356","DOIUrl":null,"url":null,"abstract":"<div><div>Analysis discriminative dictionary learning (ADDL) techniques have been studied for addressing image classification problems. However, existing ADDL methods ignore the structural dependency within the signals and cannot fit the general class of signals with irregular structures, including spherical images and 3D objects. In this paper, we propose a novel analysis discriminative convolutional graph dictionary learning method that fully exploits the structural dependency for signal classification, especially for irregular graph signals. The proposed method integrates the graph embedding information to analysis convolutional dictionary learning to derive a set of class-specific convolutional graph sub-dictionaries for extracting consistent class-specific features. An analytical decorrelation term is introduced as regularization to constrain the linear classifier for each class and improve the discrimination ability of dictionary-based sparse representation. Furthermore, we develop an efficient alternating update algorithm to solve the formulated non-convex minimization problem that simultaneously achieves sparse representation using ISTA and optimizes the convolutional graph dictionary and classifiers in an analytic manner. To our best knowledge, this paper is the first attempt to achieve analysis dictionary learning for generalized classification of signals with regular and irregular structures. Experimental results show that the proposed method outperforms state-of-the-art discriminative dictionary learning methods by 0.26% to 2.68% in classification accuracy for both regular and irregular signal classification. Notably, it is comparable to recent deep learning models with up to about 1% accuracy loss in irregular signal classification.</div></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"138 ","pages":"Article 117356"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing-Image Communication","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092359652500102X","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Analysis discriminative dictionary learning (ADDL) techniques have been studied for addressing image classification problems. However, existing ADDL methods ignore the structural dependency within the signals and cannot fit the general class of signals with irregular structures, including spherical images and 3D objects. In this paper, we propose a novel analysis discriminative convolutional graph dictionary learning method that fully exploits the structural dependency for signal classification, especially for irregular graph signals. The proposed method integrates the graph embedding information to analysis convolutional dictionary learning to derive a set of class-specific convolutional graph sub-dictionaries for extracting consistent class-specific features. An analytical decorrelation term is introduced as regularization to constrain the linear classifier for each class and improve the discrimination ability of dictionary-based sparse representation. Furthermore, we develop an efficient alternating update algorithm to solve the formulated non-convex minimization problem that simultaneously achieves sparse representation using ISTA and optimizes the convolutional graph dictionary and classifiers in an analytic manner. To our best knowledge, this paper is the first attempt to achieve analysis dictionary learning for generalized classification of signals with regular and irregular structures. Experimental results show that the proposed method outperforms state-of-the-art discriminative dictionary learning methods by 0.26% to 2.68% in classification accuracy for both regular and irregular signal classification. Notably, it is comparable to recent deep learning models with up to about 1% accuracy loss in irregular signal classification.
期刊介绍:
Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following:
To present a forum for the advancement of theory and practice of image communication.
To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems.
To contribute to a rapid information exchange between the industrial and academic environments.
The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world.
Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments.
Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.