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Pattern Recognition (PR)

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Factor de Impacto:
7.6
Editor:
Elsevier
ISSN:
0031-3203
Vistas:
153854
Seguidores:
150
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Solicitud de Artículos

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.
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Números especiales

Número especial sobre Evolving Multi-View Learning: From Theory to High-Impact Applications Día de Entrega: 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
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Número especial sobre Adaptive and Scalable Vision Models in Dynamic and Resource-Constrained Environments Día de Entrega: 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
Última actualización por Dou Sun el
Número especial sobre Agentic AI for Pattern Recognition Día de Entrega: 2027-03-31 Agentic AI has achieved remarkable success by combining the deep semantic understanding and generative capabilities of foundation models with autonomous agency, tool integration, and environmental interaction. This agentic paradigm is now catalyzing a parallel transformation in pattern recognition (PR), shifting the field beyond traditional static classification toward dynamic, interactive systems capable of autonomous decision-making. Contemporary Large Language Model (LLM) agents operate through sophisticated perception–reasoning–action loops that enable them to perceive multimodal environmental inputs, reason over complex patterns through chain-of-thought mechanisms, and execute contextually appropriate actions. This paradigm shift demands a critical re-examination of how pattern recognition systems are designed, moving from isolated task-specific models to generalist agents that can adaptively handle diverse recognition tasks through unified architectures. To this end, this special issue seeks to capture the transformative potential of PR Agent frameworks, where perception modules extract meaningful representations from raw sensory data, reasoning systems formulate adaptive strategies for pattern interpretation, and action mechanisms enable iterative refinement through environmental feedback. This special issue is founded on the premise that LLM agents and other agentic AI approaches for PR should be understood not only as a powerful set of methods, but as an emerging scientific and technical paradigm for the field. In alignment with the mission of pattern recognition research, we seek contributions that move beyond narrow performance gains to engage with the deeper disciplinary questions opened by this transition: how perception–reasoning–action loops reshape pattern perception, reasoning, recognition, interaction, and decision-making; how they alter the relationship between static feature extraction and dynamic, context-aware interpretation; and how they demand new pattern recognition frameworks for trust, robustness, interpretability, and responsible deployment. This special issue aims not merely to document an emerging trend, but to help define the next intellectual horizon of pattern recognition as a field where recognition and action are inseparably coupled through continuous perception–reasoning–action cycles, and where pattern understanding emerges from active, goal-directed engagement with dynamically changing environments. Guest editors: Assoc. Prof. Yifan Zhu (Executive Guest Editor) School of Computer Science, Beijing University of Posts and Telecommunications (BUPT), Beijing, China Email: [email protected] Assist. Prof. Kaize Shi School of Science, Engineering and Digital Technologies, University of Southern Queensland, Queensland, Australia Email: [email protected] Dr. Qika Lin National University of Singapore, Singapore Email: [email protected] Dr. Bin Pu The Hong Kong University of Science and Technology, Hong Kong, China Email: [email protected] Dr. Wenguan Wang Zhejiang University, Hangzhou, China Email: [email protected] Special issue information: We invite high-quality submissions presenting original research articles, research surveys, methods papers, and critical application studies in areas including, but not limited to: Foundations of Agentic AI for Pattern Recognition: Novel architectures and​frameworks for agent-based perception; chain-of-thought reasoning for visual andsignal analysis; tool-learning and API-usage for integrating classical PR algorithms with LLM agents and other agentic AI approaches. Unified 2D and 3D Scene Understanding Agents: Agents capable of bridging the gap between 2D image data and 3D point clouds/meshes; open-vocabulary object detection, segmentation, and grounding in complex 3D environments using language-guided reasoning. Revitalizing Classical PR Tasks with Agentic Workflows: Application of agents to traditional domains such as Optical Character Recognition (OCR), document analysis, biometrics, and handwriting recognition, specifically focusing on handling unstructured or noisy data through iterative reasoning. Spatiotemporal and Video Pattern Recognition Agents: Agents designed for temporal reasoning, action recognition, and video understanding; systems that cananswer complex queries about long-form video content by actively retrieving and analyzing frames. Embodied Agents and Active Perception: Integration of pattern recognition agents into robotic systems; active vision where agents dynamically adjust sensors or viewpoints to improve recognition confidence; navigation and manipulation based on visual reasoning. Neurosymbolic and Knowledge-Aware Agents: Hybrid systems combining neural pattern recognition with symbolic logic and knowledge graphs; agents that leverage external knowledge bases to recognize rare, zero-shot, or fine-grained categories. Multi-Agent Systems for Collaborative Recognition: Swarm intelligence and multiagent frameworks where specialized agents collaborate to solve complex, multi-modal recognition problems. Human-in-the-loop and Interactive Recognition Agents: Frameworks for collaborative pattern recognition where agents solicit human feedback to resolve ambiguity; interactive segmentation and annotation tools powered by conversational agents. Generative Data and Synthetic Training for Agents: Utilizing LLM to create synthetic 2D/3D training data or counterfactual scenarios to robustify pattern recognition agents; self-improving agents that generate their own training signals. Trustworthiness, Explainability, and Safety in Agentic PR: Methods for interpreting the decision-making process of black-box agents; ensuring robustness against adversarial attacks in agent-based recognition; ethical considerations in autonomous surveillance and biometric agents. Domain-Specific Agent Applications: Specialized agents for high-stakes pattern recognition in medical imaging (e.g., radiology report generation), remote sensing (satellite imagery analysis), industrial inspection, and bioinformatics. Manuscript submission information: Open for Submission: from 31-May-2026 to 31-Mar-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: Agentic AI for PR" - 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 the Guest Editor team. 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: Agentic AI, LLM Agent, Perception–Reasoning–Action https://www.sciencedirect.com/special-issue/334009/agentic-ai-for-pattern-recognition
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Número especial sobre Multimodal Representation Learning Based on Vision Foundation Models Día de Entrega: 2027-03-31 Recent years have witnessed remarkable progress in foundation models-large-scale neural networks pre-trained on vast amounts of data-that exhibit strong generalization and transfer capabilities across diverse tasks and domains. In the visual domain, vision foundation models (VFMs) such as CLIP, DINOv2 and Segment Anything Model (SAM), have demonstrated unprecedented performance in zero-shot recognition, segmentation, captioning, and cross-modal alignment. These models serve as powerful backbones for multimodal understanding, enabling rich, joint representations that bridge vision with language, audio, depth, motion, and other sensory signals. However, despite rapid advancements, fundamental challenges remain in how to effectively leverage VFMs for robust, efficient, and interpretable multimodal representation learning. Key open questions include: How can we align heterogeneous modalities in a unified semantic space? How do we adapt frozen or partially trainable VFMs to downstream multimodal tasks with limited labeled data? What architectural and training paradigms best support compositional reasoning, temporal modeling, or 3D scene understanding using VFMs as priors? This special issue aims to bring together cutting-edge research that explores the intersection of vision foundation models and multimodal representation learning, fostering interdisciplinary dialogue among computer vision, natural language processing, machine learning, and cognitive science communities. Guest editors: Xin Tan (Executive Guest Editor), Research Professor, East China Normal University& Shanghai AI Laboratory, Shanghai, China; Email: [email protected] Xingchen Zhang, Senior Lecturer, University of Exeter, Exeter, UK; Email: [email protected] Yadan Luo, Senior Lecturer, The University of Queensland, Brisbane, Australia; Email: [email protected] Pantea Keikhosrokiani, Adjunct Professor, University of Oulu, Oulu, Finland; Email: [email protected] Piotr Koniusz, Associate Professor, University of New South Wales, Sydney, Australia; Email: [email protected] Special issue information: This special issue will provide a timely and comprehensive forum for disseminating high-impact research at the forefront of multimodal AI. By focusing on vision foundation models as a unifying substrate, it will catalyze innovations that move beyond task-specific pipelines toward general-purpose, perception-capable AI systems. The issue is expected to attract submissions from leading academic labs and industry research groups, thereby shaping future directions in foundation model research and multimodal intelligence. Topics of interest include, but are not limited to: Architectures & Frameworks: Novel multimodal fusion strategies leveraging VFMs (e.g., late/early/hybrid fusion, cross-attention mechanisms, adapter modules). Training Paradigms: Self-supervised, contrastive, generative, or instruction-tuning approaches for aligning vision with other modalities using foundation models. Efficiency & Adaptation: Parameter-efficient fine-tuning (PEFT), distillation, quantization, or continual learning techniques for VFM-based multimodal systems. Reasoning & Compositionality: Using VFMs to support symbolic, causal, or hierarchical reasoning across modalities.3D & Embodied Multimodal Learning: Integrating VFMs with depth, LiDAR, robotics, or egocentric vision for spatial and interactive understanding. Evaluation & Benchmarks: New datasets, metrics, or evaluation protocols for assessing multimodal generalization, robustness, and fairness. Applications: Multimodal retrieval, video-language understanding, assistive technologies, medical imaging with reports, autonomous systems, etc. Ethics & Societal Impact: Bias mitigation, interpretability, privacy, and responsible deployment of VFM-powered multimodal systems. Manuscript submission information: Open for Submission: from 15-Apr-2026 to 31-Mar-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: Vision Foundation 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 the Guest Editor team. 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 Foundation Models; Representation Learning; 3D scene understanding; Embodied Multimodal Learning https://www.sciencedirect.com/special-issue/332578/multimodal-representation-learning-based-on-vision-foundation-models
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