IEEE open journal of signal processing最新文献_第6页

MAC-S$^{3}$Robo: Mamba-Driven Contrastive Semi-Supervised Surgical Robot Segmentation for Minimally Invasive Surgery MAC-S$^{3}$Robo：用于微创手术的曼巴驱动的对比半监督手术机器人分割

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1109/OJSP.2026.3713079

Chengyi Zhang;Zhihao Chen;Yiyuan Ge;Zhihao Guo;Ziyang Wang

{"title":"MAC-S$^{3}$Robo: Mamba-Driven Contrastive Semi-Supervised Surgical Robot Segmentation for Minimally Invasive Surgery","authors":"Chengyi Zhang;Zhihao Chen;Yiyuan Ge;Zhihao Guo;Ziyang Wang","doi":"10.1109/OJSP.2026.3713079","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3713079","url":null,"abstract":"Accurate and reliable surgical robot segmentation during endoscopic surgery is critical for improving robotic perception, task automation, and patient safety. Current deep learning approaches rely heavily on supervised training, requiring extensive, manually annotated datasets, which are costly and difficult to obtain in surgical contexts. To address this challenge, we propose MAC-S<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>Robo, a Mamba-driven contrastive semi-supervised segmentation framework tailored to robotic and endoscopic surgical segmentation. Specifically, it integrates two Mamba-based branches, cross pseudo-label supervision on unlabelled frames, and labelled supervision within a unified optimisation objective. Within this framework, a Mamba-based U-shaped encoder–decoder replaces U-Net convolutional blocks with visual state-space (VSS) blocks while preserving the multi-scale design and identity skip connections, enabling stronger modelling of long-range dependencies and global context. Contrastive self-supervision is integrated into the same objective to strengthen pixel-level representations and stabilise features, benefiting the semi-supervised setting. Extensive experiments on the EndoVis 2017 and 2018 benchmarks demonstrate that MAC-S<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>Robo achieves the best mean Dice among semi-supervised baselines at 10% labelled data on both datasets and maintains consistent gains as the labelled ratio increases.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"833-840"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148747905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Adaptive Volterra Filtering Based on a Generalized Gaussian Mixture Model Framework 基于广义高斯混合模型框架的鲁棒自适应Volterra滤波

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-08-24 DOI: 10.1109/OJSP.2026.3726821

Xinnian Guo;Yan Shen;Yinjie Jia;Yaowei Guo;Guobing Qian

{"title":"Robust Adaptive Volterra Filtering Based on a Generalized Gaussian Mixture Model Framework","authors":"Xinnian Guo;Yan Shen;Yinjie Jia;Yaowei Guo;Guobing Qian","doi":"10.1109/OJSP.2026.3726821","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3726821","url":null,"abstract":"In complex and impulse noise environments, traditional adaptive filtering algorithms often fail to achieve satisfactory performance due to the non-Gaussian characteristics of the noise. For the adaptive filtering problem of linear systems, existing research has applied the generalized Gaussian mixture model (GGMM) to residual modeling, leading to the development of robust filtering methods that improve performance in non-Gaussian noise environments. However, in practical applications, systems often exhibit significant nonlinearity, making it difficult for linear filters to accurately characterize the complex mapping between input and output. Building on this foundation, this paper extends the GGMM framework to nonlinear Volterra filters, proposing a robust adaptive Volterra filtering method based on the GGMM. The proposed method captures high-order nonlinear characteristics of the system through Volterra expansion. Under the maximum likelihood framework, it utilizes GGMM probability modeling to characterize the residual distribution. Meanwhile, the distribution parameters are iteratively estimated via the expectation-maximization (EM) algorithm, achieving synergistic adaptive updates of filter coefficients and noise models. Theoretical analysis and simulation results demonstrate that in non-Gaussian noise environment, the method not only maintains fast convergence and low steady-state error, but also significantly outperforms traditional linear robust filtering methods for nonlinear system identification.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"917-933"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11663398","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Display-Recaptured Document Image Detection Under Texture Interference 纹理干扰下显示重捕获文档图像检测

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-08-31 DOI: 10.1109/OJSP.2026.3729152

Peiquan Li;Rui Zheng;Changsheng Chen;Yulia Chernyshova;Dmitry Nikolaev;Shunquan Tan;Vladimir Arlazarov;Laiqun Xia

{"title":"Display-Recaptured Document Image Detection Under Texture Interference","authors":"Peiquan Li;Rui Zheng;Changsheng Chen;Yulia Chernyshova;Dmitry Nikolaev;Shunquan Tan;Vladimir Arlazarov;Laiqun Xia","doi":"10.1109/OJSP.2026.3729152","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3729152","url":null,"abstract":"Display-recapture attacks pose a critical threat to the integrity and authenticity of digital document images, particularly by concealing tampering traces through rephotographing displayed content. Existing document presentation attack detection (DPAD) methods often struggle to distinguish forensic artifacts (e.g., chromatic distortions and moiré patterns) from the natural textures inherent in documents with complex backgrounds. To address this texture confusion challenge, we propose a dual-stream LC&DF framework that integrates Local Chromaticity (LC) features with a Masked Attention mechanism and a Discriminative Frequency (DF) branch enhanced via a Frequency-domain Moiré-Aware Adapter (FMA-Ada). This architecture jointly models local chromatic distortions and global frequency cues to robustly isolate recapture-induced artifacts from genuine document content. Extensive evaluations demonstrate the superiority of our method. Under the cross-dataset protocol, LC&DF achieves state-of-the-art performance. In challenging in-the-wild scenarios evaluated on the ROD_M&F, SRDID162, DLC2021, and KID34 K benchmarks, our method consistently outperforms existing baselines, achieving an AUC of 0.9105 and reducing the Equal Error Rate by up to 15.72 percentage points on challenging document benchmarks. Furthermore, we conduct a zero-shot evaluation of Multimodal Large Language Models (MLLMs), revealing that they lack the sensitivity to subtle forensic artifacts. Visualizations of challenging samples further confirm that our claims on distinguishing the forensic artifacts from the document textures. The source code of this work is available ONLINE.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"889-901"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11673094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Behavior of Image Information Metrics Under Controlled Blur and Noise Distortions 图像信息度量在受控模糊和噪声畸变下的行为

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-21 DOI: 10.1109/OJSP.2026.3695743

Nikola Plavac;Seyedeh Kimia Arfaie Oghani;Seyed Ali Amirshahi;Sophie Triantaphilidou;Marius Pedersen

{"title":"Behavior of Image Information Metrics Under Controlled Blur and Noise Distortions","authors":"Nikola Plavac;Seyedeh Kimia Arfaie Oghani;Seyed Ali Amirshahi;Sophie Triantaphilidou;Marius Pedersen","doi":"10.1109/OJSP.2026.3695743","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3695743","url":null,"abstract":"Camera Image Quality Metrics (IQMs) are widely used to characterise imaging systems for human as well as machine vision applications, yet their relationship to the latter remains insufficiently understood. Recently introduced image information metrics have been proposed as alternatives to traditional IQMs, but their behavior under controlled imaging degradations and relevance to vision tasks require further study. This work analyzes the behavior of traditional camera IQMs and image information metrics under controlled blur and noise. We collected a dataset consisting of two parts. The first consists of laboratory captures of four objects, created with systematic camera variations in defocus, noise level, exposure value, and camera-to-object distance. The images included a test chart to enable direct measurement of imaging metrics from each scene. For the second part, a physicsbased imaging pipeline simulation was used to generate synthetic images with independently controlled blur and noise levels. Across both laboratory and simulated data, image information metrics exhibited greater sensitivity to the combined effect of optical and noise degradations compared to traditional IQMs. Multiple object detection networks were evaluated and qualitative relationships between traditional IQMs, image information metrics, and detection performance were examined. Although detection performance variations were modest, certain image information metrics, particularly the ideal observer signal-to-noise ratio, exhibited qualitatively more consistent monotonic trends than traditional IQMs. Overall, the results indicate that image information metrics show greater sensitivity to combined blur and noise degradations than traditional IQMs, while also highlighting the limitations of imaging metrics in predicting task-level performance, which remains content dependent.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"581-590"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11533421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Shadow-Aware Inverse Rendering in Gaussian Splatting 高斯溅射中阴影感知的反向渲染

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-09-18 DOI: 10.1109/OJSP.2026.3735472

Kotoka Matsunaga;Xuening Tian;Hideo Saito;Dieter Schmalstieg;Shohei Mori

{"title":"Shadow-Aware Inverse Rendering in Gaussian Splatting","authors":"Kotoka Matsunaga;Xuening Tian;Hideo Saito;Dieter Schmalstieg;Shohei Mori","doi":"10.1109/OJSP.2026.3735472","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3735472","url":null,"abstract":"Inverse rendering decomposes a scene into geometry, materials, and lighting to enable physically consistent view synthesis and relighting. Inverse rendering approaches built upon Gaussian Splatting benefit from its efficient reconstruction pipeline, yet they struggle in the presence of strong shadows. Shadows are often absorbed into albedo, leading to view synthesis in which the original shadows remain, while newly computed shadows are simply overlaid during relighting. To address this issue, we present a shadow-aware inverse rendering method for 2D Gaussian Splatting that explicitly disentangles illumination effects from surface reflectance. Our approach estimates pixelwise directional visibility in image space from observed geometry, enabling accurate modeling of cast shadows. We further introduce material segmentation, which groups shadowed and non-shadowed pixels of the same surface into a single material, enforcing consistent reflectance and preventing shadows from being baked into the albedo. Our method produces shadow-free albedo and models shadows as directional attenuation, enabling stable and high-fidelity relighting under novel illumination.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"1021-1029"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11695671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-Form Least-Squares Design of Fast-Convolution Based Variable-Bandwidth FIR Filters 基于快速卷积变带宽FIR滤波器的闭型最小二乘设计

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 DOI: 10.1109/OJSP.2025.3650439

Oksana Moryakova;Håkan Johansson

{"title":"Closed-Form Least-Squares Design of Fast-Convolution Based Variable-Bandwidth FIR Filters","authors":"Oksana Moryakova;Håkan Johansson","doi":"10.1109/OJSP.2025.3650439","DOIUrl":"https://doi.org/10.1109/OJSP.2025.3650439","url":null,"abstract":"This paper introduces a closed-form least-squares (LS) design approach for fast-convolution (FC) based variable-bandwidth (VBW) finite-impulse-response (FIR) filters. The proposed LS design utilizes frequency sampling and the VBW filter frequency-domain implementation using the overlap-save (OLS) method, that together offer significant savings in implementation and online bandwidth reconfiguration complexities. Since combining frequency-domain design and OLS implementation leads to a linear periodic time-varying (LPTV) behavior of the VBW filter, a set of the corresponding time-invariant impulse responses is considered in the proposed design. Through numerical examples, it is demonstrated that the proposed approach enables not only closed-form design of FC-based VBW filters with substantial complexity reductions compared to existing solutions for a given performance, but also allows the variable bandwidth range to be extended without any increase in complexity. Moreover, a way of reducing the maximum approximation error energy over the whole set of the time-invariant filters of the LPTV system is shown by introducing appropriate weighting functions in the design.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"54-63"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11321300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Spiral Scanning for Confocal Non-Line-of-Sight Imaging 自适应螺旋扫描共聚焦非视距成像

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-04-27 DOI: 10.1109/OJSP.2026.3688052

Tomoya Oyama;Yang Dixin;Mariko Isogawa

{"title":"Adaptive Spiral Scanning for Confocal Non-Line-of-Sight Imaging","authors":"Tomoya Oyama;Yang Dixin;Mariko Isogawa","doi":"10.1109/OJSP.2026.3688052","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3688052","url":null,"abstract":"Non-line-of-sight (NLOS) imaging aims to reconstruct objects that are hidden from direct view by utilizing light reflected from a relay surface. Conventional NLOS imaging methods typically rely on uniform raster scanning of the relay surface, which poses a challenge due to the long acquisition times. To address this challenge, researchers have proposed various scanning trajectories, such as circular and spiral patterns. However, these methods rely on predefined scanning trajectories. As a result, scanning points cannot be adjusted according to the intensity distribution on the relay surface. When hidden objects are spatially localized, these methods do not allocate sufficient sampling to regions with strong return intensity, leading to reduced reconstruction quality. In this paper, we propose Dynamic Archimedean Spiral Confocal NLOS (DAS-CNLOS), which dynamically selects scanning trajectories based on the observed return intensity. Our method efficiently captures the return intensity from hidden objects using a limited number of scanning points. This is achieved by adaptively shifting the spiral center toward high-intensity regions based on a sequentially estimated intensity distribution. Furthermore, for 2D reconstruction, we introduce Voronoi Density Compensation (VDC) to correct sampling density non-uniformity induced by dynamic spiral scanning, enabling robust and high quality reconstructions. Experimental results demonstrate that our DAS-CNLOS achieves superior 2D reconstruction quality compared to conventional circular scanning and spiral scanning methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"482-491"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11495079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VeriTrace: A Multitask Disaster Dataset for Real–Fake Verification and Source Tracing VeriTrace：用于真假验证和源跟踪的多任务灾难数据集

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-04-15 DOI: 10.1109/OJSP.2026.3684389

Surbhi Madan;Yusuke Yasuda;Junichi Yamagishi

{"title":"VeriTrace: A Multitask Disaster Dataset for Real–Fake Verification and Source Tracing","authors":"Surbhi Madan;Yusuke Yasuda;Junichi Yamagishi","doi":"10.1109/OJSP.2026.3684389","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3684389","url":null,"abstract":"The rapid increase in AI-generated images is affecting not only face-focused domains like face swapping and media manipulation but also non-facial content such as scenes and objects. Yet, one crucial area that remains underexplored is disaster deepfakes. Natural disasters such as earthquakes, floods, and wildfires occur unpredictably, and the widespread use of social media has amplified the circulation of fake disaster images that can mislead the public about the severity and consequences of these events. Detecting whether a disaster image is real or AI-generated is challenging due to the semantic complexity of disaster scenes, the scarcity of labeled data, and the difficulty of identifying images produced by previously unseen generative models. We argue that the limited diversity of existing training data is a key barrier to building robust detection systems. To address this, we introduce VeriTrace, a large-scale, in-the-wild multitask dataset curated via a CLIP-based pipeline containing AI-generated disaster images from 3,191 generative models. VeriTrace supports both deepfake detection and source tracing. Our benchmarks confirm that disaster-domain deepfakes are significantly harder to detect than human-domain. Furthermore, we identify an identification-attribution gap: while high-fidelity commercial images frequently evade standard detectors, they possess distinct architectural “fingerprints” that make them highly susceptible to source tracing. VeriTrace and its associated code will be released upon acceptance.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"438-446"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11481973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QANet: A Neural Framework for Quality Assessment of Instance Segmentation in Microscopy Imaging QANet：一种用于显微成像实例分割质量评估的神经网络框架

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-06-11 DOI: 10.1109/OJSP.2026.3702522

Amit Aharoni;Assaf Arbelle;Michael Sidorov;Eliav Elul;Tammy Riklin Raviv

{"title":"QANet: A Neural Framework for Quality Assessment of Instance Segmentation in Microscopy Imaging","authors":"Amit Aharoni;Assaf Arbelle;Michael Sidorov;Eliav Elul;Tammy Riklin Raviv","doi":"10.1109/OJSP.2026.3702522","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3702522","url":null,"abstract":"Reliable instance segmentation of cells in microscopy images is essential for quantitative imaging analysis, yet estimating segmentation quality on unseen data, though fundamental, remains largely overlooked. In practice, performance assessment typically requires manual ground-truth annotations, which are costly and impractical at scale. We address this evaluation gap by introducing QANet, a neural framework for post-hoc quality assessment of instance segmentations. Unlike segmentation models, QANet does not predict masks; instead, it receives an image and a segmentation produced by any method and estimates a quantitative quality score that approximates standard evaluation metrics, without requiring ground-truth annotations at inference time. QANet is model-agnostic and formulated as a regression task over image–mask pairs. It is built on the RibCage architecture, which performs multi-scale comparison between image content and segmentation structure, enabling sensitivity to both global shape consistency and fine boundary errors. Training is performed using synthetically perturbed segmentations with known quality scores, enabling supervision across controlled error modes while eliminating the need for large annotated failure datasets. We evaluate QANet on 2D and 3D datasets from the Cell Segmentation Benchmark and demonstrate accurate prediction of both overlap-based measures and boundary-sensitive metrics across multiple segmentation methods.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"659-667"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11557600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Optimal Segmentation Algorithm for Large-Scale Time Series Applications 一种新的大规模时间序列最优分割算法

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-15 DOI: 10.1109/OJSP.2026.3713804

Chi-Jen Wu;Wei-Sheng Zeng;Jan-Ming Ho;Jerry Chun-Wei Lin

{"title":"A Novel Optimal Segmentation Algorithm for Large-Scale Time Series Applications","authors":"Chi-Jen Wu;Wei-Sheng Zeng;Jan-Ming Ho;Jerry Chun-Wei Lin","doi":"10.1109/OJSP.2026.3713804","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3713804","url":null,"abstract":"When dealing with a time series dataset, a key challenge lies in grasping the dynamic signals. An integral task involves representing the time series through line segments, typically done as a preprocessing step to capture patterns and signals. This paper, however, centers on optimizing the time series segmentation through segmented linear regression models, specifically addressing large-scale datasets. We first formulate the optimization of Multi-Segment Linear Regression (MSLR), which aims to minimize the global square error of the segmented linear representations. The major contribution is that we introduce an optimal MSLR algorithm, denoted as OMSLR, featuring a 2-level dynamic programming structure. We also demonstrate the optimality and the low time complexity of O(<inline-formula><tex-math>$kn^{2}$</tex-math></inline-formula>), where <inline-formula><tex-math>$k$</tex-math></inline-formula> is the number of non-overlapping segments and <inline-formula><tex-math>$n$</tex-math></inline-formula> is the length of the time series. The results demonstrate that the designed OMSLR matches the optimal global squared error of the classical <inline-formula><tex-math>$O(kn^{3})$</tex-math></inline-formula> dynamic programming baseline and brute-force search to floating-point precision, while running roughly two orders of magnitude faster at <inline-formula><tex-math>$n = 10{,}000$</tex-math></inline-formula> and producing lower reconstruction error at every <inline-formula><tex-math>$k$</tex-math></inline-formula> than the bottom-up, top-down, Sim-Piece, and Mix-Piece baselines. Both effectiveness and efficiency are demonstrated on synthetic series, reference benchmarks, and the UCR time series archive.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"809-821"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11610874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0