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Mathematics
Special Issues
Machine Learning and Mathematical Methods in Computer Vision
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Machine Learning and Mathematical Methods in Computer Vision

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 727

Special Issue Editor

Dr. Long Lan

E-Mail Website
Guest Editor
Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China
Interests: mathematical methods; machine learning; computer vision; image processing; object detection; multiple object tracking

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the intersection of machine learning and mathematical methods in computer vision, highlighting their vital role in enhancing the performance and reliability of visual data processing algorithms. Mathematical modeling provides a rigorous framework for addressing complex challenges in computer vision, enabling the development of robust and efficient algorithms that can analyze and interpret vast amounts of visual information. The synergy between machine learning techniques and mathematical principles not only fosters innovative approaches to existing problems but also encourages interdisciplinary collaborations, resulting in breakthroughs that can significantly advance the computer vision field.

We invite authors to contribute original research articles, reviews, and case studies that demonstrate novel applications of machine learning and mathematical methodologies in computer vision. Topics of interest include, but are not limited to, advanced mathematical techniques for image processing, model optimization, and novel machine learning architectures tailored for visual data analysis.

Should you have any questions or need an extension, please feel free to contact me directly. Your contributions will be invaluable in shaping the future of this dynamic and evolving field. Thank you for considering this opportunity, and we look forward to receiving your submissions.

Dr. Long Lan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical methods
  • machine learning
  • computer vision
  • image processing
  • object detection
  • multiple object tracking
  • image segmentation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 942 KiB  
Article
Pseudo-Multiview Learning Using Subjective Logic for Enhanced Classification Accuracy
by Dat Ngo
Mathematics 2025, 13(13), 2085; https://doi.org/10.3390/math13132085 - 25 Jun 2025
Viewed by 114
Abstract
Deep learning has significantly advanced image classification by leveraging hierarchical feature representations. A key factor in enhancing classification accuracy is feature concatenation, which integrates diverse feature sets to provide a richer representation of input data. However, this fusion strategy has inherent limitations, including [...] Read more.
Deep learning has significantly advanced image classification by leveraging hierarchical feature representations. A key factor in enhancing classification accuracy is feature concatenation, which integrates diverse feature sets to provide a richer representation of input data. However, this fusion strategy has inherent limitations, including increased computational complexity, susceptibility to redundant or irrelevant features, and challenges in optimally weighting different feature contributions. To address these challenges, this paper presents a pseudo-multiview learning method that dynamically combines different views at the evidence level using a belief-based model known as subjective logic. This approach adaptively assigns confidence levels to each view, ensuring more effective integration of complementary information while mitigating the impact of noisy or less relevant features. Experimental evaluations of datasets from various domains demonstrate that the proposed method enhances classification accuracy and robustness compared with conventional classification techniques. Full article
(This article belongs to the Special Issue Machine Learning and Mathematical Methods in Computer Vision)
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15 pages, 4572 KiB  
Article
A Focus on Important Samples for Out-of-Distribution Detection
by Jiaqi Wan, Guoliang Wen, Guangming Sun, Yuntian Zhu and Zhaohui Hu
Mathematics 2025, 13(12), 1998; https://doi.org/10.3390/math13121998 - 17 Jun 2025
Viewed by 207
Abstract
To ensure the reliability and security of machine learning classification models when deployed in the open world, it is crucial that these models can detect out-of-distribution (OOD) data that exhibits semantic shifts from the in-distribution (ID) data used during training. This necessity has [...] Read more.
To ensure the reliability and security of machine learning classification models when deployed in the open world, it is crucial that these models can detect out-of-distribution (OOD) data that exhibits semantic shifts from the in-distribution (ID) data used during training. This necessity has spurred extensive research on OOD detection. Previous methods required a large amount of finely labeled OOD data for model training, which is costly or performed poorly in open-world scenarios. To address these limitations, we propose a novel method named focus on important samples (FIS) in this paper. FIS leverages model-predicted OOD scores to identify and focus on important samples that are more beneficial for model training. By learning from these important samples, our method aims to achieve reliable OOD detection performance while reducing training costs and the risk of overfitting training data, thereby enabling the model to better distinguish between ID and OOD data. Extensive experiments across diverse OOD detection scenarios demonstrate that FIS achieves superior performance compared to existing approaches, highlighting its robust and efficient OOD detection performance in practical applications. Full article
(This article belongs to the Special Issue Machine Learning and Mathematical Methods in Computer Vision)
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