Pattern Recognition最新文献_第3页

Improving adversarial transferability via semantic-style joint expectation perturbations 通过语义式联合期望扰动提高对抗可迁移性

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-25 DOI: 10.1016/j.patcog.2025.112474

Zhi Lin , Bingwen Wang , Xixi Wang , Yu Zhang , Xiao Wang , Kang Deng , Anjie Peng , Jin Tang , Xing Yang

{"title":"Improving adversarial transferability via semantic-style joint expectation perturbations","authors":"Zhi Lin , Bingwen Wang , Xixi Wang , Yu Zhang , Xiao Wang , Kang Deng , Anjie Peng , Jin Tang , Xing Yang","doi":"10.1016/j.patcog.2025.112474","DOIUrl":"10.1016/j.patcog.2025.112474","url":null,"abstract":"<div><div>Style and content information, which are model-independent inherent properties of an image, serve as crucial information that deep neural networks depend on for classification tasks. However, most existing gradient-based attacks mainly distort content-related information through semantic distortion of the model’s final output, neglecting the role of style information. To fully distort the inherent intrinsic information of the image, this paper proposes Semantic-Style joint Expectation Perturbations (SSEPs). Specifically, we first establish a style loss based on the kernel function from the feature space of the surrogate model and inject it into gradient-based attacks to form a Semantics-Style joint Loss (SSL) for generating joint perturbations. Subsequently, we use gradient normalization and the proposed dynamic gradient decomposition scheme to address the problems of multi-objective gradient magnitude differences and gradient conflicts that occur in SSL during optimization. Finally, we generate SSEPs by motivating the maximization of the expected loss, thereby enhancing the transferability of Adversarial Examples (AEs). On the ImageNet sub-dataset, extensive experiments show that AEs covered with SSEPs have high transferability. Compared to the baseline attack (MI-FGSM), our method achieves at least a 14 % and 5 % higher attack success rate for normally trained models and defense models, respectively. Compared with other classic and advanced gradient-based attacks and feature-level attacks, our method still has advantages in attack performance. Our code is available at: <span><span>https://github.com/OUTOFTEN/TransferAttack-ssep</span><svg><path></path></svg></span></div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112474"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving lesion segmentation in medical images by global and regional feature compensation 基于全局和区域特征补偿的医学图像病灶分割方法

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-25 DOI: 10.1016/j.patcog.2025.112461

Chuhan Wang , Zhenghao Chen , Jean Y.H. Yang , Jinman Kim

{"title":"Improving lesion segmentation in medical images by global and regional feature compensation","authors":"Chuhan Wang , Zhenghao Chen , Jean Y.H. Yang , Jinman Kim","doi":"10.1016/j.patcog.2025.112461","DOIUrl":"10.1016/j.patcog.2025.112461","url":null,"abstract":"<div><div>Automated lesion segmentation of medical images has made tremendous improvements in recent years due to deep learning advancements. However, accurately capturing fine-grained global and regional feature representations remains a challenge. Many existing methods achieve suboptimal performance in complex lesion segmentation due to information loss during typical downsampling operations and insufficient capture of either regional or global features. To address these issues, we propose the Global and Regional Compensation Segmentation Framework (GRCSF), which introduces two key innovations: the Global Compensation Unit (GCU) and the Region Compensation Unit (RCU). The proposed GCU addresses resolution loss in the U-shaped backbone by preserving global contextual features and fine-grained details during multiscale downsampling. Meanwhile, the RCU introduces a self-supervised learning (SSL) residual map generated by Masked Autoencoders (MAE), obtained as pixel-wise differences between reconstructed and original images, to highlight regions with potential lesions. These SSL residual maps guide precise lesion localization and segmentation through a patch-based cross-attention mechanism that integrates regional spatial and pixel-level features. Additionally, the RCU incorporates patch-level importance scoring to enhance feature fusion by leveraging global spatial information from the backbone. Experiments on three publicly available medical image segmentation datasets, including brain stroke lesion, lung tumor and coronary artery calcification datasets, demonstrate that our GRCSF outperforms state-of-the-art methods, confirming its effectiveness across diverse lesion types and its potential as a generalizable lesion segmentation solution.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112461"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAR image change detection based on saliency region guidance and SIFT keypoint extraction 基于显著区制导和SIFT关键点提取的SAR图像变化检测

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-25 DOI: 10.1016/j.patcog.2025.112471

Lu Wang , Bailiang Sun , Chunhui Zhao , Suleman Mazhar , Tomoaki Ohtsuki , P. Takis Mathiopoulos , Fumiyuki Adachi

{"title":"SAR image change detection based on saliency region guidance and SIFT keypoint extraction","authors":"Lu Wang , Bailiang Sun , Chunhui Zhao , Suleman Mazhar , Tomoaki Ohtsuki , P. Takis Mathiopoulos , Fumiyuki Adachi","doi":"10.1016/j.patcog.2025.112471","DOIUrl":"10.1016/j.patcog.2025.112471","url":null,"abstract":"<div><div>Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT’s stable description of local features with ViT’s modeling capability for global features, improving the model’s accuracy and robustness.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112471"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Salient object ranking with reinforcement learning 强化学习显著性对象排序

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-25 DOI: 10.1016/j.patcog.2025.112499

Qi Gao , Heng Li , Jianpin Chen , Xinyu Chai

{"title":"Salient object ranking with reinforcement learning","authors":"Qi Gao , Heng Li , Jianpin Chen , Xinyu Chai","doi":"10.1016/j.patcog.2025.112499","DOIUrl":"10.1016/j.patcog.2025.112499","url":null,"abstract":"<div><div>Objects in an image inherently draw attention due to their vivid colors and larger sizes, indicating their high saliency. The salient object ranking (SOR) task aims to prioritize multiple salient objects within a scene based on their saliency levels. Past research has predominantly treated the SOR task as a static process, typically formulating it as a regression or classification problem. However, these approaches overlook the dynamic nature of human attention, which shifts as context and inter-object correlations influence focus. To address this, we employ a reinforcement learning strategy, modeling SOR as a dynamic iterative sequence process. We train an actor to select salient objects from the environment. Additionally, we design a reward strategy that encourages the selection of the most prominent object among those not previously chosen. The selection order generated by the actor directly determines the ranking of object saliency in the scene. Furthermore, we identify limitations in existing SOR evaluation metrics, which may falter in certain scenarios. To address this, we introduce a simple and useful metric, referred to as the F1-Sor, into SOR tasks, improving the evaluation accuracy of the SOR tasks. Our model achieves state-of-the-art performance on publicly available SOR datasets.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112499"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts 强分布转移挑战分子诱导癌细胞生长抑制的适形预测

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112501

Saiveth Hernandez-Hernandez , Qianrong Guo , Pedro J. Ballester

{"title":"Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts","authors":"Saiveth Hernandez-Hernandez , Qianrong Guo , Pedro J. Ballester","doi":"10.1016/j.patcog.2025.112501","DOIUrl":"10.1016/j.patcog.2025.112501","url":null,"abstract":"<div><div>The drug discovery process often employs phenotypic and target-based virtual screening to identify potential drug candidates. Despite the longstanding dominance of target-based approaches, phenotypic virtual screening is undergoing a resurgence due to its potential being now better understood. In the context of cancer cell lines, a well-established experimental system for phenotypic screens, molecules are tested to identify their whole-cell activity, as summarized by their half-maximal inhibitory concentrations. Machine learning has emerged as a potent tool for computationally guiding such screens, yet important research gaps persist, including generalization and uncertainty quantification. To address this, we leverage a clustering-based validation approach, called Leave Dissimilar Molecules Out (LDMO). This strategy enables a more rigorous assessment of model generalization to structurally novel compounds. This study focuses on applying Conformal Prediction (CP), a model-agnostic framework, to predict the activities of novel molecules on specific cancer cell lines. A total of 4320 independent models were evaluated across 60 cell lines, 5 CP variants, 2 set features, and training-test splits, providing strong and consistent results. From this comprehensive evaluation, we concluded that, regardless of the cell line or model, novel molecules with smaller CP-calculated confidence intervals tend to have smaller predicted errors once measured activities are revealed. It was also possible to anticipate the activities of dissimilar test molecules across 50 or more cell lines. These outcomes demonstrate the robust efficacy that LDMO-based models can achieve in realistic and challenging scenarios, thereby providing valuable insights for enhancing decision-making processes in drug discovery.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112501"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-AD: cross-domain unsupervised anomaly detection for medical and industrial applications Multi-AD：用于医疗和工业应用的跨域无监督异常检测

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112486

Wahyu Rahmaniar, Kenji Suzuki

{"title":"Multi-AD: cross-domain unsupervised anomaly detection for medical and industrial applications","authors":"Wahyu Rahmaniar, Kenji Suzuki","doi":"10.1016/j.patcog.2025.112486","DOIUrl":"10.1016/j.patcog.2025.112486","url":null,"abstract":"<div><div>Traditional deep learning models often lack annotated data, especially in cross-domain applications such as anomaly detection, which is critical for early disease diagnosis in medicine and defect detection in industry. To address this challenge, we propose Multi-AD, an unsupervised convolutional neural network (CNN) model for robust anomaly detection across medical and industrial domain images. Our approach utilizes the squeeze-and-excitation (SE) block to enhance feature extraction by applying channel-wise attention, enabling the model to focus on the most relevant features and detect subtle anomalies. Additionally, knowledge distillation (KD) transfers informative features from the teacher to the student model, enabling effective learning of the differences between normal and anomalous data. Then, the discriminator network further enhances the model’s capacity to distinguish between normal and anomalous data. At the inference stage, by integrating multi-scale features, the student model gains the ability to detect anomalies of varying sizes. Teacher-student (<em>T</em>-<em>S</em>) architecture ensures consistency in representing high-dimensional features while adapting these features to improve anomaly detection. Multi-AD was evaluated on several medical datasets, including brain MRI, liver CT, and retina OCT, as well as industrial datasets, such as MVTec AD, demonstrating strong generalization across multiple domains. Experimental results demonstrated that our approach consistently outperformed state-of-the-art models, achieving the best average accuracy for anomaly localization at both the image level (81.4 % for medical and 99.6 % for industrial) and pixel level (97.0 % for medical and 98.4 % for industrial), making it effective for real-world applications.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112486"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-layer processing and coarse filtering network for accurate feature matching 一个多层处理和粗过滤网络，用于精确的特征匹配

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112464

Yuan Guo , Wenpeng Li , Ping Zhai

{"title":"A multi-layer processing and coarse filtering network for accurate feature matching","authors":"Yuan Guo , Wenpeng Li , Ping Zhai","doi":"10.1016/j.patcog.2025.112464","DOIUrl":"10.1016/j.patcog.2025.112464","url":null,"abstract":"<div><div>The core task of feature matching is establishing correspondences between two images. The methods based on Transformers have achieved impressive results, which can directly capture the relationships among all features without relying on the distances between them. However, it also reduce the weight of long-distance texture features and ignore simultaneous integration of global, local, and multi-scale features, leading to limited matching accuracy. To address this issue, we propose a detector-free feature matching method based on Transformer with multi-level processing and coarse-grained filtering. First, we apply a local window aggregation module to minimize irrelevant interference through window attention and combine local self-attention with global self-attention to ensure the features have a global perspective but not lose local details. Then, the multi-scale features are processed in layers, integrating multi-scale information into the matching phase, allowing each layer to perform feature matching at different scales for more precise matches. Additionally, we designed a filter to discard incorrectly matched points in the global context, thereby improving the accuracy of the matching points. Extensive experiments demonstrate that our method delivers excellent results comparing with the current state-of-the-art techniques in the tasks of pose estimation, homography estimation, and visual localization. Compared with the baseline method LoFTR, our method achieves an average improvement of 16.07 % in pose estimation, 6.52 % in homography estimation, and 9.69 % in visual localization. Meanwhile, our method also demonstrates superior performance compared to other state-of-the-art feature matching approaches.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112464"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-level noise augmentation for graph clustering with triplet-wise contrastive learning 基于三重智能对比学习的双水平噪声增强图聚类

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112463

Tianxiang Zhao , Youqing Wang , Shilong Xu , Tianchuan Yang , Junbin Gao , Jipeng Guo

{"title":"Dual-level noise augmentation for graph clustering with triplet-wise contrastive learning","authors":"Tianxiang Zhao , Youqing Wang , Shilong Xu , Tianchuan Yang , Junbin Gao , Jipeng Guo","doi":"10.1016/j.patcog.2025.112463","DOIUrl":"10.1016/j.patcog.2025.112463","url":null,"abstract":"<div><div>Contrastive deep graph clustering has attracted widespread attention due to its self-supervised representation learning mechanism and excellent clustering performance. Although, most existing methods rely on low-pass filtering to achieve denoising, ignoring the potential benefit of high-frequency information and noise in obtaining comprehensive and robust representation. Second, commonly used contrastive learning strategies generally treat non-target samples as negative samples, which is prone to triggering contrastive bias and weakening the representation quality. To this end, this paper proposes a novel contrastive graph clustering framework, Dual-level Noise Augmentation for Graph Clustering with triplet-wise Contrastive learning (DNA-CGC), strengthening the benefit of noise to enrich the representation learning and amplify the contrastive learning efficacy. It consists of two core modules, Hybrid Noise Representation Augmentation (HNRA) and Noise-Aware Contrastive Learning (NACL). The HNRA module integrates low- and high-frequency graph signals to capture both shared and distinctive node characteristics, while introducing Gaussian noise as beneficial perturbation to enrich the representation diversity, thereby achieving multi-information fusion under hybrid noise. The NACL module, on the other hand, generates exclusive negative samples through Gaussian noise and constructs the triplet-wise contrastive pairs (Target, Positive, Negative), mitigating the contrastive bias by preventing false negatives and further facilitating more accurate semantic alignment. Extensive experiments on six benchmark datasets validate the significant advantages of DNA-CGC in terms of clustering performance and representation quality. The code could be available at <span><span>https://github.com/TianxiangZhao0474/DNA-CGC.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112463"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedFAT: Frequency adpative interpolation for federated domain generalization on heterogeneous medical images 基于频率自适应插值的异质医学图像联邦域泛化

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112459

Donghao Wang , Yingchun Cui , Mingyang Li , Heran Xi , Jinghua Zhu

{"title":"FedFAT: Frequency adpative interpolation for federated domain generalization on heterogeneous medical images","authors":"Donghao Wang , Yingchun Cui , Mingyang Li , Heran Xi , Jinghua Zhu","doi":"10.1016/j.patcog.2025.112459","DOIUrl":"10.1016/j.patcog.2025.112459","url":null,"abstract":"<div><div>Multiple distributed medical institutions can leverage federated learning (FL) to collaboratively build a shared prediction model with privacy protection. However, the presence of non-independent and identically distributed (non-IID) data in medical imaging leads to data drift in practical learning scenarios, detrimentally affecting both convergence and generalization to the unseen domain. In this paper, we propose a novel framework named Federated Frequency Adaptive Interpolation(FedFAT), which leverages a frequency space adaptive interpolation mechanism to mitigate data drift in federated domain generalization. FedFAT enables clients to adaptively exchange partial amplitude information, leveraging multi-source data distributions to enhance generalization. Crucially, local phase information is retained to preserve privacy. To mitigate data drift, FedFAT employs cross-client feature alignment via amplitude normalization, which effectively batch-normalizes images from diverse source distributions. Furthermore, we introduce a client-specific weight perturbation mechanism designed to guide local models toward a consistent low-loss region. We have theoretically analyzed the proposed method and empirically conducted extensive experiments on two medical image classification and segmentation tasks, showing that FedFAT outperforms a set of recent state-of-the-art methods with average Dice improvement of 2.42 % and 10.61 % on the prostate MRI segmentation datasets (PROMISE12, PROSTATEx, and NCI-ISBI) and breast cancer classification datasets(CAMELYON17), respectively. FedFAT also has an improvement of 4.15 % on generalization performance. These results demonstrate the superiority of FedFAT in handling data drift and improving generalization performance.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112459"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hypergraph regularization-based anchor learning for multi-view clustering 基于超图正则化的多视图聚类锚点学习

IF 7.6 1区计算机科学

Pattern Recognition Pub Date : 2025-09-24 DOI: 10.1016/j.patcog.2025.112465

Yunpeng Zeng , Peng Song , Beihua Yang , Changjia Wang , Guanghao Du , Yanwei Yu , Wenming Zheng

{"title":"Hypergraph regularization-based anchor learning for multi-view clustering","authors":"Yunpeng Zeng , Peng Song , Beihua Yang , Changjia Wang , Guanghao Du , Yanwei Yu , Wenming Zheng","doi":"10.1016/j.patcog.2025.112465","DOIUrl":"10.1016/j.patcog.2025.112465","url":null,"abstract":"<div><div>Current anchor graph-based multi-view clustering methods can effectively address the problem of high computational cost for clustering large-scale multimedia data. However, they have the following shortcomings: (1) The relationships between anchor points are not adequately considered. (2) The correlations between the consistent anchor graph and diverse anchor graphs are ignored. To handle these issues, we propose a novel multi-view clustering method named Hypergraph Regularization-Based Anchor Learning (HRFAL). Specifically, we first process the original data to obtain a consistent anchor graph and diverse anchor graphs, which can explore more comprehensive consistent and complementary information. Meanwhile, the hyper-Laplacian regularization is applied to the anchor points to explore the higher-order relationships between the anchor points, thus enabling the generation of high-quality anchor graphs. Furthermore, the orthogonal diversity constraints are imposed on the consistent and diverse anchor graphs to enhance the distinction between the consistent and diverse components, resulting in better exploitation of consistent and complementary information. Finally, the Schatten <span><math><mi>p</mi></math></span>-norm constraint is implemented on the consistent anchor graph to maintain its low-rank structure, thus obtaining more robust consistent information. Experimental results on eight multi-view datasets show that HRFAL exhibits superior performance in terms of accuracy and speed.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112465"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0