Who publishes Pattern Recognition?

Pattern Recognition is published by Elsevier.

What is the ISSN of Pattern Recognition?

The ISSN of Pattern Recognition is 0031-3203.

What is the impact factor of Pattern Recognition?

The reported impact factor of Pattern Recognition is 7.6. Impact factor values are reproduced from publishers and may lag the latest JCR release.

What is the CCF ranking of Pattern Recognition?

Pattern Recognition is ranked CCF B in the China Computer Federation journal catalogue.

期刊信息

Pattern Recognition (PR)

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影响因子:

7.6

出版商:

Elsevier

ISSN:

0031-3203

浏览:

132172

关注:

148

征稿

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.

最后更新 Dou Sun 在 2025-12-18

Special Issues

Special Issue on Next-Generation Audio-Visual Generation Models for Multimodal Perception and Pattern Recognition
截稿日期: 2026-05-31

In recent years, audio-visual generation has emerged as a transformative research area at the intersection of pattern recognition, computer vision, and audio signal processing. The development of generative models—such as GANs, VAEs, diffusion models, and transformer-based architectures—has significantly advanced the ability to learn joint audio-visual representations, synthesise realistic multimodal content, and enhance cross-modal understanding.

As we enter the era of multimodal artificial intelligence, next-generation audio-visual generation models are not only reshaping tasks such as video-to-speech synthesis, audio-driven facial animation, and cross-modal translation, but also pushing the boundaries of representation learning, semantic alignment, and data efficiency in pattern recognition.

This special issue aims to gather recent advances and cutting-edge research in audio-visual generation that enhance multimodal perception and pattern understanding. We seek high-quality contributions that present novel methods, theoretical insights, or real-world applications that demonstrate the power and potential of generative audio-visual models.
We invite submissions on topics including, but not limited to:

    Audio-visual generative models for pattern recognition
    Diffusion, GAN, and transformer-based architectures for audio-visual synthesis
    Cross-modal generation and translation (e.g., speech-to-video, video-to-audio)
    Audio-driven talking face and lip-sync generation
    Multimodal disentanglement and controllable generation
    Learning with limited or weakly-labelled multimodal data
    Representation learning and latent space modeling for audio-visual content
    Temporal alignment and synchronization in generated audio-visual sequences
    Evaluation metrics for generated multimodal content
    Applications in healthcare, education, entertainment, surveillance, and HCI
    Benchmarks, datasets, and reproducible frameworks for audio-visual generation

Guest editors:

Prof. Ognjen Arandjelovic
Affiliation: University of St Andrews, St Andrews, UK

Prof. Weiwei Jiang
Affiliation: Beijing University of Posts and Telecommunications, Beijing, China

Manuscript submission information:

    Open for Submission: from 16-Jan-2026 to 31-May-2026
    Submission Site: Editorial Manager®
    Article Type Name: "VSI: PR_Multimodal Perception and Pattern Recognition" - 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. Ognjen Arandjelovic.
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:

    LLM; 
    VLM; 
    generative; 
    machine learning

https://www.sciencedirect.com/special-issue/329506/next-generation-audio-visual-generation-models-for-multimodal-perception-and-pattern-recognition

最后更新 Dou Sun 在 2026-03-11

Special Issue on Multimodal Pattern Recognition for Biomedical Data: Theories, Algorithms and Applications
截稿日期: 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

最后更新 Dou Sun 在 2026-03-11

Special Issue on Advancements in Multi-Source Heterogeneous Data Fusion for Pattern Recognition
截稿日期: 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

最后更新 Dou Sun 在 2025-12-18

Special Issue on Evolving Multi-View Learning: From Theory to High-Impact Applications
截稿日期: 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

最后更新 Dou Sun 在 2025-12-18

Special Issue on Adaptive and Scalable Vision Models in Dynamic and Resource-Constrained Environments
截稿日期: 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

最后更新 Dou Sun 在 2025-12-18

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CCF	全称	影响因子	出版商	ISSN
a	IEEE Transactions on Dependable and Secure Computing	7.5	IEEE	1545-5971
b	Information Sciences	6.8	Elsevier	0020-0255
c	Neurocomputing	6.5	Elsevier	0925-2312
	IEEE Security & Privacy	3.0	IEEE	1540-7993
a	IEEE Transactions on Pattern Analysis and Machine Intelligence	20.8	IEEE	0162-8828
b	International Journal of Human-Computer Interaction	4.9	Taylor & Francis	1044-7318
c	IEEE Transactions on Computational Social Systems	4.9	IEEE	2373-7476
c	Future Generation Computer Systems	6.1	Elsevier	0167-739X
b	Advanced Engineering Informatics	9.9	Elsevier	1474-0346
c	Engineering Applications of Artificial Intelligence	8.0	Elsevier	0952-1976

CCF	全称	影响因子	出版商	ISSN
	International Journal of Pattern Recognition & Artificial Intelligence		World Scientific	0218-0014
	Engineering	11.6	Elsevier	2095-8099
c	Pattern Recognition Letters	3.9	Elsevier	0167-8655
b	IEEE Transactions on Multimedia	9.7	IEEE	1520-9210
b	Software & Systems Modeling	3.2	Springer	1619-1366
b	IEEE Transactions on Neural Networks and Learning Systems	8.9	IEEE	1045-9227
b	Information Sciences	6.8	Elsevier	0020-0255
b	Computer Networks	4.6	Elsevier	1389-1286
b	Computers & Security	5.4	Elsevier	0167-4048
b	IEEE Transactions on Intelligent Transportation Systems	8.4	IEEE	1524-9050

CCF	CORE	QUALIS	简称	全称	截稿日期	通知日期	会议日期
a	a*	a1	Ubicomp	ACM International Joint Conference on Pervasive and Ubiquitous Computing	2026-05-17	2026-07-01	2026-10-13
c			PRCV	Chinese Conference on Pattern Recognition and Computer Vision	2026-04-30	2026-06-20	2026-08-22
b		b4	BIBM	International Conference on Bioinformatics & Biomedicine	2026-07-05	2026-09-25	2026-12-01
b	a	a1	ISMB	International Conference on Intelligent Systems for Molecular Biology	2026-04-09	2026-05-05	2026-07-12
b	a	a1	ACSAC	Annual Computer Security Applications Conference	2026-05-26	2026-07-13	2026-12-07
a	a*	a1	NDSS	Annual Network & Distributed System Security Symposium	2026-08-19	2026-11-04	2027-03-22
a	a*	a1	CCS	ACM Conference on Computer and Communications Security	2026-04-22	2026-07-17	2026-11-15
b	a*	a1	ICDM	International Conference on Data Mining	2026-06-06	2026-08-16	2026-11-12
			MDA-AI&PR	International Conference on Mass Data Analysis of Images and Signals in Artificial Intelligence and Pattern Recognition	2022-03-15	2022-04-18	2022-07-11
			CLEO-PR	Pacific Rim Conference on Lasers and Electro-Optics	2024-02-28	2024-04-22	2024-08-04

期刊信息

Pattern Recognition (PR)

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Special Issues

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