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International Journal of Multimedia Information Retrieval - Springer Nature | 2024 Impact Factor:2.9 | Cite Score:8.1 | Q1

International Journal of Multimedia Information Retrieval

Impact Factor and Journal Rank of International Journal of Multimedia Information Retrieval

  • About: International Journal of Multimedia Information Retrieval is a scholarly journal dedicated to the publication of high-quality research in the field of multimedia information retrieval. The journal focuses on the theoretical foundations, algorithms, methodologies, and applications of multimedia information retrieval systems. It provides a platform for researchers, practitioners, and policymakers to share insights and advancements in this rapidly evolving field.
  • Focus Areas: The journal covers a broad range of topics including: Multimedia Search and Retrieval Image, Video, and Audio Retrieval Multimodal Information Fusion Semantic Analysis and Understanding Content-Based Retrieval User Interfaces and Interaction Techniques Machine Learning for Multimedia Retrieval Evaluation and Benchmarking of Multimedia Systems Applications in Healthcare, Security, Entertainment, and Education Big Data and Multimedia Analytics
  • Publication Impact: International Journal of Multimedia Information Retrieval significantly contributes to the advancement of knowledge and practice in the field of multimedia information retrieval. By publishing original research, reviews, and technical papers, the journal ensures that the latest developments and practical applications are disseminated to the global community.
  • Peer Review Process: Articles published in the journal undergo a rigorous peer-review process to ensure high quality, relevance, and scientific rigor. This process helps maintain the journal standards and ensures that significant contributions to the field are published.
  • Significance: By providing a platform for the dissemination of innovative research in multimedia information retrieval, International Journal of Multimedia Information Retrieval fosters innovation and collaboration. The journal interdisciplinary approach promotes the integration of diverse perspectives and methodologies, enhancing the understanding and application of multimedia information retrieval technologies.

  • Editor-in-Chief:  Michael Lew

  • Scope: The International Journal of Multimedia Information Retrieval is a peer-reviewed publication dedicated to the dissemination of research and advancements in the field of multimedia information retrieval (MIR). It provides a platform for researchers, practitioners, and engineers to present their latest findings, innovations, and applications related to the retrieval and analysis of multimedia data. The journal covers a broad range of topics essential to the advancement of MIR technologies. Here is an overview of its key focus areas and scope:
  • 1. Multimedia Content Analysis:
    Research on methods and algorithms for analyzing multimedia content, including images, videos, audio, and text.
    Studies on feature extraction, pattern recognition, and semantic analysis of multimedia data.
    Advances in techniques for multimedia content understanding and interpretation.
  • 2. Multimedia Search and Retrieval:
    Exploration of algorithms and systems for efficient search and retrieval of multimedia content.
    Research on similarity measures, indexing, and ranking methods for multimedia data.
    Advances in relevance feedback, query formulation, and search optimization in MIR systems.
  • 3. Machine Learning and AI for MIR:
    Studies on the application of machine learning and artificial intelligence techniques in multimedia information retrieval.
    Research on deep learning, neural networks, and other AI methods for MIR.
    Advances in the use of AI for improving accuracy, efficiency, and user experience in MIR systems.
  • 4. Multimodal and Cross-modal Retrieval:
    Exploration of techniques for integrating and retrieving information across multiple modalities, such as text, image, video, and audio.
    Research on cross-modal matching, alignment, and fusion for multimedia data.
    Advances in multimodal interaction and user interfaces for MIR systems.
  • 5. User Modeling and Personalization:
    Studies on user behavior modeling and personalization techniques in multimedia information retrieval.
    Research on user profiling, context-aware retrieval, and adaptive MIR systems.
    Advances in personalized recommendation systems and user-centric MIR applications.
  • 6. Evaluation and Benchmarking:
    Exploration of methodologies and metrics for evaluating the performance of MIR systems.
    Research on benchmarking datasets, evaluation protocols, and comparative studies in MIR.
    Advances in developing standardized evaluation frameworks and tools for MIR research.
  • 7. Applications and Case Studies:
    Studies on the application of multimedia information retrieval in various domains, such as healthcare, education, entertainment, and security.
    Research on real-world case studies and practical implementations of MIR systems.
    Advances in domain-specific MIR solutions and interdisciplinary applications.
  • 8. Emerging Technologies and Trends:
    Exploration of emerging technologies and future trends in multimedia information retrieval.
    Research on new paradigms, such as augmented reality, virtual reality, and social media analysis in MIR.
    Advances in anticipating and addressing future challenges in the field of multimedia information retrieval.
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  • Print ISSN:  2192-6611

    Electronic ISSN:  2192-662X

  • Abstracting and Indexing:  Scopus

  • Imapct Factor 2024:  2.9

  • Subject Area and Category:   Computer Science, Information Systems, Engineering, Media Technology, Social Sciences, Library and Information Sciences

  • Publication Frequency:  

  • H Index:  33

  • Best Quartile:

    Q1:  Information Systems

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  8.1

  • SNIP:  1.573

  • Journal Rank(SJR):  0.882