IEEE Transactions on Multimedia

Top Research Topics at IEEE Transactions on Multimedia?

IEEE Transactions on Multimedia covers a variety of subjects, including Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Computer network. The journal focuses on Artificial intelligence research which is adjacent to topics in Machine learning. The journal holds forums on Pattern recognition that merges themes from other disciplines such as Artificial neural network and Contextual image classification, Image (mathematics), Image retrieval.

The journal dives deep in exploring the relationship between the study of Image retrieval and Information retrieval. The journal covers various topics on Computer vision such as Image segmentation, Video tracking, Image processing, Pixel and Data compression. The discussions emphasized the topic of Image segmentation in an attempt to further explore the field of Segmentation.

The journal focused on Feature extraction research but expanded to cover Object detection. The research on Computer network tackled can also make contributions to studies in the areas of Wireless network, Real-time computing and Distributed computing. Research on Real-time computing addressed in the journal frequently intersections with the field of Video quality.

• Artificial intelligence (51.66%)
• Pattern recognition (22.74%)
• Computer vision (22.32%)

What are the most cited papers published in the journal?

• An SVD-based watermarking scheme for protecting rightful ownership (840 citations)
• A Measurement Study of a Large-Scale P2P IPTV System (830 citations)
• Rate-distortion optimized streaming of packetized media (672 citations)

Research areas of the most cited articles at IEEE Transactions on Multimedia:

The most cited publications focus on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Computer network. The most cited articles dive deep in exploring the relationship between the study of Artificial intelligence and Machine learning. The studies on Pattern recognition discussed at the journal articles can also contribute to research in the domains of Contextual image classification and Image (mathematics).

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Operating system
• Computer network

The previous edition focused in particular on these issues:

The foci of IEEE Transactions on Multimedia are Artificial intelligence, Pattern recognition, Feature extraction, Computer vision and Feature (computer vision). The work tackled in the journal goes beyond the discipline of Artificial intelligence as it also encompasses Machine learning. The journal explores topics in Pattern recognition which can be helpful for research in disciplines like Feature (machine learning), Artificial neural network, Representation (mathematics), Benchmark (computing) and Robustness (computer science).

The journal addresses concerns in Feature extraction which are intertwined with other disciplines, such as Object (computer science), Object detection, Feature learning and Convolutional neural network. The journal features Computer vision research that overlaps with concepts in Frame (networking). The research on Visualization featured in IEEE Transactions on Multimedia combines topics in other fields like Semantics, Task analysis and Natural language processing.

The most cited articles from the last journal are:

• Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss (32 citations)
• Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019 (30 citations)
• Coarse-to-Fine CNN for Image Super-Resolution (27 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IEEE Transactions on Multimedia (based on the number of publications) are:

• Changsheng Xu (67 papers) published 19 papers at the last edition, 13 more than at the previous edition,
• Qi Tian (51 papers) published 7 papers at the last edition, 4 more than at the previous edition,
• Weisi Lin (37 papers) published 4 papers at the last edition, 2 more than at the previous edition,
• Wen Gao (35 papers) published 2 papers at the last edition, 1 less than at the previous edition,
• Meng Wang (34 papers) published 4 papers at the last edition, 2 more than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IEEE Transactions on Multimedia (based on the number of publications) are:

• Chinese Academy of Sciences (214 papers) published 46 papers at the last edition, 14 more than at the previous edition,
• Nanyang Technological University (152 papers) published 13 papers at the last edition, 6 more than at the previous edition,
• Microsoft (141 papers) published 4 papers at the last edition, 1 more than at the previous edition,
• Tsinghua University (126 papers) published 8 papers at the last edition, 8 less than at the previous edition,
• University of Science and Technology of China (118 papers) published 31 papers at the last edition, 12 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 20.10% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 33.26% were posted by at least one author from the top 10 institutions publishing in the journal. Another 20.90% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.12% of all publications and 27.72% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Contributing to Career Development in Multimedia Research

The IEEE Transactions on Multimedia Journal, in addition to its groundbreaking research and studies, also plays a significant role in shaping the careers of aspiring researchers, scholars, and professionals in the field of multimedia. Whether you're interested in artificial intelligence, computer vision, pattern recognition, or any of the other fascinating topics covered in the journal, you can use the published research as a stepping stone for your career development. For example, if you're aspiring to become a preschool teacher while maintaining interest in multimedia research, integrating the insights from IEEE Transactions on Multimedia in your lesson plans can potentially introduce young learners to these advanced topics at an early age. If you're wondering, "how do you become a preschool teacher in New York?", our linked resource can guide you through the necessary steps. This career advice not only applies to those aiming for academia. Professionals involved in tech startups, corporate R&D labs, and even government institutions can build upon the knowledge disseminated by the journal. The field of multimedia is vast and continually growing, and the research presented in our journal provides a wealth of information just waiting to be applied in real-world scenarios. Remember, staying updated with the latest research trends helps you stay competitive in the job market. The more you know about current research in your field, the better your prospects for professional growth and career advancement. Reading and understanding the research in IEEE Transactions on Multimedia can be an excellent way to begin.