Journal Information

Pattern Recognition (PR)

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Impact Factor:
7.6
Publisher:
Elsevier
ISSN:
0031-3203
Viewed:
132558
Tracked:
148

Call For Papers

Pattern Recognition (PR) is an academic journal published by Elsevier. (ISSN 0031-3203, impact factor 7.6, CCF B).

Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably. The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages.
Last updated by Dou Sun in

Special Issues

Special Issue on Multimodal Pattern Recognition for Biomedical Data: Theories, Algorithms and Applications Submission Date: 2026-08-31 The rapid advancement of medical technologies has led to an explosion of multimodal medical data, including genomic sequences, electronic health records (EHRs), medical imaging (MRI, CT, PET), wearable sensor data, and multi-omics profiles. Integrating and interpreting these diverse data types presents both unprecedented opportunities and significant challenges in precision medicine, disease diagnosis, and personalized treatment. The integration of multimodal medical data has the potential to revolutionize healthcare by enabling more accurate diagnoses, personalized treatment strategies, and improved patient outcomes. However, effectively combining these diverse data types-each with distinct formats, dimensionalities, and temporal resolutions, requires novel computational approaches. Traditional single-modal analysis methods often fail to capture the complex interactions between different data modalities, necessitating the development of advanced AI techniques that can extract synergistic information across modalities. This special issue aims to serve as a comprehensive resource for researchers, clinicians, and industry professionals working at the intersection of AI and multimodal medical data. With the increasing adoption of AI in healthcare, there is a pressing need for robust, generalizable, and interpretable models that can handle the complexity of real-world medical data. By bringing together diverse perspectives, i.e., from computational innovation to clinical implementation. This issue will facilitate cross-disciplinary collaboration and accelerate the translation of AI research into tangible solutions for the area of medicine. Guest editors: Prof. Xinwang Liu National University of Defense Technology, Changsha, China Assoc. Prof. Chang Tang Huazhong University of Science and Technology, Wuhan, China Dr. Siwei Wang Academy of Military Sciences, Beijing, China Prof. Lei Wang University of Wollongong, Wollongong, Australia Assoc. Prof. Junbo Ma Hangzhou Dianzi University, Hangzhou, China Manuscript submission information: Open for Submission: from 01-Feb-2026 to 31-Aug-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: PR_Biomedical Data" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor Prof. Xinwang Liu. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier. For more information about our Journal, please visit our ScienceDirect Page: Pattern Recognition | Journal | ScienceDirect.com by Elsevier. Keywords: Multimodal learning Medical data processing Multi-omics Multimodal generative models https://www.sciencedirect.com/special-issue/329765/multimodal-pattern-recognition-for-biomedical-data-theories-algorithms-and-applications
Last updated by Dou Sun in
Special Issue on Advancements in Multi-Source Heterogeneous Data Fusion for Pattern Recognition Submission Date: 2026-09-30 The exponential growth of heterogeneous data sources—images, video, audio, sensor signals, time-series, graphs, and IoT streams—has created an urgent demand for effective multi-source data fusion. Researchers and practitioners now face the challenge of integrating disparate modalities that are often asynchronous, noisy, and incomplete. Traditional approaches remain insufficient for such heterogeneity. More advanced, scalable, and trustworthy methods are needed to ensure robustness, adaptability, and interpretability in real-world systems, including healthcare, autonomous driving, robotics, smart cities, and environmental monitoring. At the same time, large foundation models (LLMs, VLMs, MLLMs) are opening unprecedented opportunities for multimodal representation learning and cross-domain transfer. Yet, their application to heterogeneous fusion still requires solving issues of modality alignment, temporal/spatial synchronization, schema mismatches, and uncertainty management. This special issue will highlight the latest advances in multi-source fusion, spanning theoretical foundations, algorithms, architectures, and practical deployments. It will provide a venue for showcasing robust, efficient, and explainable solutions that can transform pattern recognition research and industry. Topics of Interest include, but are not limited to: Fusion algorithms for multi-source heterogeneous data Efficient and scalable fusion architectures Handling data heterogeneity, incompleteness, and misalignment Uncertainty, causality, and interpretability in data fusion Trustworthy, fair, and responsible fusion systems Multimodal learning with foundation models Evaluation metrics, datasets, and benchmarking protocols Applications in healthcare, robotics, transportation, and smart cities Emerging trends in cross-domain and multimodal data fusion Guest editors: Dr. Xiaohan Yu Macquarie University, Sydney, Australia Prof. Dr. Xiao Bai Beihang University, Beijing, China Dr. Imad Rida BMBI - Biomecanique et Bioingenierie, Compiegne, France Prof. Amir Hussain Edinburgh Napier University, Edinburgh, United Kingdom Manuscript submission information: Open for Submission: from 1-Mar-2026 to 30-Sep-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: PR_Heterogeneous Data Fusion" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Dr. Xiaohan Yu). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Pattern Recognition | Journal | ScienceDirect.com by Elsevier Keywords: multi-source heterogeneous data cross-domain and multimodal multimodal learning
Last updated by Dou Sun in
Special Issue on Evolving Multi-View Learning: From Theory to High-Impact Applications Submission Date: 2026-10-31 With the proliferation of data from diverse sources and the rapid advancements in computational capabilities, the field of multi-view learning has become increasingly vital. This domain focuses on developing sophisticated models to harness the rich, complementary information present across different data modalities or "views", aiming to achieve more robust and comprehensive data representations. In recent years, deep learning and Large Language Model (LLM) techniques have unlocked new potential, enabling the capture of complex, non-linear relationships and leading to breakthroughs in a wide array of applications. This special issue invites cutting-edge research and innovative contributions that advance the theoretical foundations, methodological developments, and real-world applications of multi-view learning. We welcome submissions that explore new frontiers, including but not limited to, advanced representation alignment, novel deep architectures, and scalable algorithms on multi-view learning area. We particularly encourage papers that demonstrate the practical utility of these models in high-impact areas such as bioinformatics, intelligent systems, medical diagnostics, and beyond. This special issue will focus on: Heterogeneous Data Fusion: Effectively fusing and aligning information from different views. Low quality multi-view learning: incomplete multi-view learning, partially aligned multi-view learning, and noisy multi-view learning. Cross-modal Retrieval: Enabling precise information retrieval across views. Scalable Multi-view Algorithm. Explainability and Robustness. Multimodal Generative Models: Using models for cross-view content generation. Multimodal Representation Learning: Learning unified or correlated representations for different views. Deep Multimodal Architectures: Applying deep learning to capture complex, non-linear relationships in multi-view data. Few-shot and Zero-shot Learning: Generalizing to new tasks with limited or no labeled multi-view data. Multimodal for Applications: Applying multimodal learning to tasks like disease diagnosis, industrial vision and others. Evaluation Metrics and Benchmarks: Establishing new standards for evaluating multimodal/multi-view learning performance. Guest editors: Dr. Jie Wen Harbin Institute of Technology, Shenzhen, China Dr. Shizhe Hu Zhengzhou University, Zhengzhou, China Dr. Lusi Li Old Dominion University, Norfolk, United States
Last updated by Dou Sun in
Special Issue on Adaptive and Scalable Vision Models in Dynamic and Resource-Constrained Environments Submission Date: 2026-11-30 In today’s rapidly evolving world, vision models are playing an increasingly crucial role in a variety of applications, including robotics, autonomous driving, healthcare, industrial automation, and environmental monitoring. However, these models often face challenges in dynamic, complex, and resource-constrained environments where data is noisy, incomplete, or continuously evolving. Traditional deep learning models typically rely on vast amounts of labeled data and significant computational resources, making them less feasible in real-world settings. As the demand for more adaptable and scalable systems grows, the integration of advanced learning techniques such as few-shot learning (FSL), zero-shot learning (ZSL), incremental learning (IL), and continual learning (CL) has emerged as a promising approach to overcome these limitations. These techniques allow vision systems to operate effectively even with limited data and evolving tasks, making them ideal for dynamic and resource-constrained environments. This special issue aims to gather cutting-edge research on adaptive and scalable visual models that can work efficiently in dynamic and resource limited environments. We welcome contributions from theoretical and applied research to address major challenges such as data scarcity, computational limitations, and task development. The paper should explore new algorithms, models, and frameworks to push the limits of visual system possibilities while ensuring practical applications in real-world scenarios. Few-Shot and Zero-Shot Learning for Vision Systems Incremental and Continual Learning for Vision Efficient Vision Systems for Real-World Applications Cross-Domain and Cross-Modal Vision Systems Adaptability in Dynamic and Unstructured Environments Few-Shot and Zero-Shot Learning in Robotics and Autonomous Systems Long-Term and Lifelong Vision Learning Scalable Vision Architectures for Large-Scale Data Vision-Based Healthcare Systems Robust Vision Systems in Adverse Conditions 3D Vision Systems and Applications Transfer Learning and Knowledge Sharing Across Tasks and Domains Guest editors: Prof. Xin Ning Affiliation: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China Prof. Prayag Tiwari Affiliation: Halmstad University, Halmstad, Sweden Prof. Qiuhong Ke Affiliation: Monash University, Melbourne, Australia Prof. Sahraoui Dhelim Affiliation: Dublin City University, Dublin, Ireland Prof. Wenbin Zhang Affiliation: Florida International University, Miami, USA Manuscript submission information: Open for Submission: from 01-Jan-2026 to 30-Nov-2026 Submission Site: Editorial Manager® Article Type Name: "VSI: PR_Adaptive and Scalable Vision Models" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor Prof. Xin Ning. Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Pattern Recognition - ISSN 0031-3203 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Pattern Recognition | Journal | ScienceDirect.com by Elsevier Keywords: Vision Models; Resource-constrained Environments; Few-Shot Learning; Incremental and Continual Learning; 3D Vision Systems
Last updated by Dou Sun in

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