| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 1 | 1321 | 2369 | 161 |
The journal primarily tackles Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence focuses on Artificial intelligence research which is adjacent to topics in Machine learning. Pixel, Iterative reconstruction, Motion estimation, Object detection and Cognitive neuroscience of visual object recognition studies are all carried out as a component of the study in Computer vision presented.
The work on Pattern recognition tackled in IEEE Transactions on Pattern Analysis and Machine Intelligence brings together disciplines like Contextual image classification, Facial recognition system, Feature (computer vision) and Cluster analysis. It focuses on Algorithm as well as the interrelated topic of Mathematical optimization. Scale-space segmentation is a key component of Image segmentation research discussed in IEEE Transactions on Pattern Analysis and Machine Intelligence.
The published articles focus largely on the fields of Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image processing. The most cited papers facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Algorithm and Machine learning. Contextual image classification and Cluster analysis are some topics wherein Pattern recognition research discussed in the most cited publications has an impact.
The objective of IEEE Transactions on Pattern Analysis and Machine Intelligence is to combine knowledge in the areas of Artificial intelligence, Pattern recognition, Algorithm, Artificial neural network and Deep learning. While Artificial intelligence is the focus of the journal, it also provided insights into the studies of Machine learning, Task analysis and Computer vision. In particular, the Computer vision works presented emphasize discussions on Iterative reconstruction.
In addition to Pattern recognition research, the journal aims to explore topics under Object (computer science), Image (mathematics) and Feature (computer vision). Algorithm research presented in the journal encompasses a variety of subjects, including Outlier and Robustness (computer science). IEEE Transactions on Pattern Analysis and Machine Intelligence addresses concerns in Artificial neural network which are intertwined with other disciplines, such as Contextual image classification and Inference.
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.
The top authors publishing in IEEE Transactions on Pattern Analysis and Machine Intelligence (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Pattern Analysis and Machine Intelligence (based on the number of publications) are:
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.
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, 6.67% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.34% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.42% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.92% of all publications and 43.31% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Considering the increasing interest and advancements in Artificial Intelligence, Computer Vision, Pattern Recognition, and related fields, there are numerous career opportunities and research positions available for interested individuals. These jobs and positions can range from being a Software Engineer specializing in AI, to working as a Research Scientist focusing on Computer Vision, and even teaching these topics as a high school or university educator. Career paths can be highly flexible, tailored to an individual's interests, educational background, and skill set. In terms of education, prospective AI researchers or teachers are recommended to have a strong foundation in Mathematics and Computer Science. A Master's or Ph.D. degree in a related field is preferable for those aiming for advanced research positions. If you enjoy sharing knowledge and inspiring younger generations, becoming a teacher in a related field might be worth considering. For instance, if History is your field of interest along with Computer Science, you could aim to be a high school history teacher in Alabama. It might not strictly deal with Pattern Analysis or AI, but such roles can uniquely blend your diverse interests together and make your professional life more fulfilling. Remember, every profession contributes to the broad field of knowledge in its unique way. This blend of disciplines, also seen in research topics discussed in IEEE Transactions on Pattern Analysis and Machine Intelligence, underlines the importance and potential of interdisciplinary studies and the wide range of career opportunities it brings. Hence, whether you aim to directly delve into the depths of AI research, or chart a unique course that binds different subjects, the opportunities are vast and waiting to be explored.
Tsung-Yi Lin;Priya Goyal;Ross Girshick;Kaiming He
(2020)Kaiming He;Georgia Gkioxari;Piotr Dollar;Ross Girshick
(2020)Zhe Cao;Gines Hidalgo;Tomas Simon;Shih-En Wei
(2021)Jingdong Wang;Ke Sun;Tianheng Cheng;Borui Jiang
(2021)Shang-Hua Gao;Ming-Ming Cheng;Kai Zhao;Xin-Yu Zhang
(2021)Shervin Minaee;Yuri Y. Boykov;Fatih Porikli;Antonio J Plaza
(2021)Yulan Guo;Hanyun Wang;Qingyong Hu;Hao Liu
(2021)Longlong Jing;Yingli Tian
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