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Mathematical Methods for Pattern Recognition

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 18282

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Special Issue Editors

Dr. Chengyou Wang

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Guest Editor

School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Interests: intelligent information processing; digital image/video processing and analysis (transform coding, digital watermarking, forgery detection, etc.); computer vision; deep learning; artificial intelligence; wireless communication technology

Dr. Xiao Zhou

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Guest Editor

School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Interests: intelligent information processing; wireless communication technology (channel estimation); image communication; digital image/video processing and analysis (transform coding, digital watermarking, forgery detection, etc.); computer vision; artificial intelligence

Dr. Zhaobin Wang

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Guest Editor

School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: remote sensing image processing and its application; intelligent information processing and analysis for biomedical images; artificial neural networks; deep learning and pattern recognition; embedded system design and development

Dr. Yingchun Guo

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Guest Editor

School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
Interests: image and video processing and analysis; artificial intelligence; image and video retargeting; visual perception and image measurement; image and video saliency detection; intelligent information processing

Special Issue Information

Dear Colleagues,

Pattern recognition is the process of processing and analyzing various forms of information that characterizes things or phenomena in order to describe, identify, classify, and explain them, and is an important part of information science and artificial intelligence. Deep learning is the ruling approach in the field of pattern recognition, achieving breakthroughs in the field. However, for advanced perception, such as more complex reasoning processes across modalities or tasks or semantic-based applications (answering questions, human-computer interactions, autonomous driving decisions, etc.), some progress has been achieved, but still its implementation has not been improved upon till today. Therefore, combining mathematical methods for pattern recognition and understanding and using the results for decision planning is a promising direction requiring intensive future research.

This Special Issue accepts high-quality, original research papers and reviews describing breakthroughs in pattern recognition through novel mathematical theories or methods, as well as those using new methods to obtain better practical applications. Papers detailing interesting/significant new applications of machine learning, deep learning, and artificial intelligence are also welcome. Potential topics include, but are not limited to, the following:

Pattern recognition and its application;
Statistical methods in machine learning, deep learning, and artificial intelligence;
Natural language processing (machine translation, human-computer interaction, speech recognition, text matching, text generation, text classification, sequence labeling, image caption, sentiment analysis, etc.);
Computer vision and robot vision (feature extraction, object tracking, object detection, visual saliency detection, facial expression recognition, face recognition, fingerprint recognition, palmprint recognition, annotation, segmentation, classification, retrieval, etc.);
Robotics and robotic systems (human-robot interaction, task planning, path planning, SLAM, multisensor data fusion, robot system modeling, multirobot systems, fault detection, self-calibration and self-repair for robots, robot learning and adaptation, humanoids, industrial robotics, etc.);
Multimedia information processing and analysis (image/video transform, filtering, denoising, compression coding, deblocking, deblurring, dehazing, enhancement, restoration and reconstruction, inpainting, registration and fusion, interpolation, demosaicking, super-resolution, motion estimation, motion compensation, quality assessment, mathematical morphology, etc.);
Multimedia information security (information hiding, forensics, digital watermarking, forgery detection, steganography and steganalysis, deepfake, face spoofing detection, etc.);
Artificial intelligence security (adversarial example, model protection, privacy protection, data poisoning, backdoor attack and defense, etc.);
Cyberspace security (cryptography and applications, big data security, advanced computer security in the context of big data, public opinion analysis, etc.);
Wireless communications and networks (reconfigurable intelligence surface (RIS), dense heterogeneous network, nonorthogonal multiple access (NOMA), deep learning-based end-to-end wireless communication, terahertz (THz) spectrum, millimeter wave communication, air-ground integrated communication, edge computing, mobile cloud computing, cooperative communication, massive MIMO, full-spectrum communication, channel coding, channel estimation, internet security, IoT, wireless sensor networks, etc.).

Dr. Chengyou Wang
Dr. Xiao Zhou
Dr. Zhaobin Wang
Dr. Yingchun Guo
Guest Editors

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Keywords

pattern recognition
machine learning
deep learning
artificial intelligence
natural language processing
computer vision
multimedia information processing and analysis
multimedia information security
cyberspace security
wireless communications and networks

Published Papers (9 papers)

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Research

Jump to: Review

19 pages, 446 KiB

Open AccessArticle

An Algorithm for Computing All Rough Set Constructs for Dimensionality Reduction

by Yanir González-Díaz, José Fco. Martínez-Trinidad, Jesús A. Carrasco-Ochoa and Manuel S. Lazo-Cortés

Mathematics 2024, 12(1), 90; https://doi.org/10.3390/math12010090 - 27 Dec 2023

Viewed by 736

Abstract

In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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22 pages, 1793 KiB

Open AccessArticle

A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics

by Canghai Song, Xiao Zhou, Chengyou Wang and Zhun Ye

Mathematics 2023, 11(15), 3342; https://doi.org/10.3390/math11153342 - 30 Jul 2023

Viewed by 716

Abstract

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in time-varying multipath fading channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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23 pages, 3226 KiB

Open AccessArticle

A Frequency Attention-Based Dual-Stream Network for Image Inpainting Forensics

by Hongquan Wang, Xinshan Zhu, Chao Ren, Lan Zhang and Shugen Ma

Mathematics 2023, 11(12), 2593; https://doi.org/10.3390/math11122593 - 06 Jun 2023

Viewed by 889

Abstract

The rapid development of digital image inpainting technology is causing serious hidden danger to the security of multimedia information. In this paper, a deep network called frequency attention-based dual-stream network (FADS-Net) is proposed for locating the inpainting region. FADS-Net is established by a dual-stream encoder and an attention-based blue-associative decoder. The dual-stream encoder includes two feature extraction streams, the raw input stream (RIS) and the frequency recalibration stream (FRS). RIS directly captures feature maps from the raw input, while FRS performs feature extraction after recalibrating the input via learning in the frequency domain. In addition, a module based on dense connection is designed to ensure efficient extraction and full fusion of dual-stream features. The attention-based associative decoder consists of a main decoder and two branch decoders. The main decoder performs up-sampling and fine-tuning of fused features by using attention mechanisms and skip connections, and ultimately generates the predicted mask for the inpainted image. Then, two branch decoders are utilized to further supervise the training of two feature streams, ensuring that they both work effectively. A joint loss function is designed to supervise the training of the entire network and two feature extraction streams for ensuring optimal forensic performance. Extensive experimental results demonstrate that the proposed FADS-Net achieves superior localization accuracy and robustness on multiple datasets compared to the state-of-the-art inpainting forensics methods. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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16 pages, 93317 KiB

Open AccessArticle

DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking

by Yimeng Zhao, Chengyou Wang, Xiao Zhou and Zhiliang Qin

Mathematics 2023, 11(1), 209; https://doi.org/10.3390/math11010209 - 31 Dec 2022

Cited by 5 | Viewed by 3051

Abstract

At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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17 pages, 4742 KiB

Open AccessArticle

Signal Identification of Wire Breaking in Bridge Cables Based on Machine Learning

by Guangming Li, Heming Ding, Yaohan Li, Chun-Yin Li and Chi-Chung Lee

Mathematics 2022, 10(19), 3690; https://doi.org/10.3390/math10193690 - 09 Oct 2022

Cited by 1 | Viewed by 1250

Abstract

With the booming development of bridge construction, bridge operation and maintenance have always been major issues to ensure the safety of the community. Affected by the long-term service of bridges and natural factors, the safety and durability of cables can be threatened. Cables are critical stress-bearing elements of large bridges such as cable-stayed bridges. Realizing the health monitoring of bridge cables is the key to ensuring the normal operation of bridges. Acoustic emission (AE) is a dynamic nondestructive testing method that is increasingly used in the local monitoring of bridge cables. In this paper, a testbed is described for generating the acoustic emission signals for signal identification testing with machine learning (ML) models. Owing to the limited number of measured signals being available, an algorithm is proposed to simulate acoustic emission signals for model training. A multi-angle feature extraction method is proposed to extract the acoustic emission signals and construct a comprehensive feature vector to characterize the acoustic emission signals. Seven ML models are trained with the simulated acoustic emission signals. Long short-term memory (LSTM) has been specially applied for deep learning demonstration which requires a large amount of training data. As all machine learning models (including LSTM) provide desired performance, it shows that the proposed approach of simulating acoustic emission signals can be effective. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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17 pages, 752 KiB

Open AccessEditor’s ChoiceArticle

Kernel Matrix-Based Heuristic Multiple Kernel Learning

by Stanton R. Price, Derek T. Anderson, Timothy C. Havens and Steven R. Price

Mathematics 2022, 10(12), 2026; https://doi.org/10.3390/math10122026 - 11 Jun 2022

Cited by 4 | Viewed by 1671

Abstract

Kernel theory is a demonstrated tool that has made its way into nearly all areas of machine learning. However, a serious limitation of kernel methods is knowing which kernel is needed in practice. Multiple kernel learning (MKL) is an attempt to learn a new tailored kernel through the aggregation of a set of valid known kernels. There are generally three approaches to MKL: fixed rules, heuristics, and optimization. Optimization is the most popular; however, a shortcoming of most optimization approaches is that they are tightly coupled with the underlying objective function and overfitting occurs. Herein, we take a different approach to MKL. Specifically, we explore different divergence measures on the values in the kernel matrices and in the reproducing kernel Hilbert space (RKHS). Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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Review

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30 pages, 814 KiB

Open AccessReview

Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

by Yun Yu, Jinhao Wang, Xiao Zhou, Chengyou Wang, Zhiquan Bai and Zhun Ye

Mathematics 2023, 11(14), 3235; https://doi.org/10.3390/math11143235 - 23 Jul 2023

Cited by 1 | Viewed by 2024

Abstract

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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33 pages, 1210 KiB

Open AccessReview

Review of Image Forensic Techniques Based on Deep Learning

by Chunyin Shi, Luan Chen, Chengyou Wang, Xiao Zhou and Zhiliang Qin

Mathematics 2023, 11(14), 3134; https://doi.org/10.3390/math11143134 - 16 Jul 2023

Cited by 4 | Viewed by 3400

Abstract

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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41 pages, 10917 KiB

Open AccessReview

Review of GrabCut in Image Processing

by Zhaobin Wang, Yongke Lv, Runliang Wu and Yaonan Zhang

Mathematics 2023, 11(8), 1965; https://doi.org/10.3390/math11081965 - 21 Apr 2023

Cited by 4 | Viewed by 2215

Abstract

As an image-segmentation method based on graph theory, GrabCut has attracted more and more researchers to pay attention to this new method because of its advantages of simple operation and excellent segmentation. In order to clarify the research status of GrabCut, we begin with the original GrabCut model, review the improved algorithms that are new or important based on GrabCut in recent years, and classify them in terms of pre-processing based on superpixel, saliency map, energy function modification, non-interactive improvement and some other improved algorithms. The application status of GrabCut in various fields is also reviewed. We also experiment with some classical improved algorithms, including GrabCut, LazySnapping, OneCut, Saliency Cuts, DenseCut and Deep GrabCut, and objectively analyze the experimental results using five evaluation indicators to verify the performance of GrabCut. Finally, some existing problems are pointed out and we also propose some future work. Full article

(This article belongs to the Special Issue Mathematical Methods for Pattern Recognition)

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