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Applied Sciences
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Application of Artificial Intelligence in Visual Signal Processing
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Application of Artificial Intelligence in Visual Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 10387

Special Issue Editors

Dr. Zhaoqiang Xia

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
Interests: visual computing; image search; image recognition
Dr. Haixi Zhang

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
Interests: deep learning; computer vision; large scale image classification; plant identification
Dr. Jun Wu

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
Interests: biomedical image analysis; feature similarity measurement; machine learning

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit a paper to the Special Issue titled “Application of Artificial Intelligence in Visual Signal Processing” devoted to Applied Sciences. This Special Issue aims to collect the latest full-length research and reviews in the areas of visual signal processing and artificial intelligence. In particular, this Special Issue focuses on the intelligent processing, analysis, and recognition with images, videos, or multimedia signals with deep learning, adversarial learning, and other approaches that have recently been gaining attention.

Topics of interest include but are not limited to the following:

  • General visual signal processing and analysis;
  • Machine learning approaches for visual signal;
  • Multimedia signal processing, analysis, and searching;
  • Visual signal processing techniques for varous fields, such as agriculture, medicine, biology, and other science domains;
  • Applications on various sensors and platforms such as vehicles and robotics with visual signal processing techniques.

Dr. Zhaoqiang Xia
Dr. Haixing Zhang
Dr. Jun Wu
Guest Editors

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. Applied Sciences 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 2400 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

  • visual signal processing
  • learning based approach
  • deep learning
  • multimedia signal processing
  • intelligent algorithms

Published Papers (7 papers)

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Research

18 pages, 2663 KiB  
Article
Breaking the ImageNet Pretraining Paradigm: A General Framework for Training Using Only Remote Sensing Scene Images
by Tao Xu, Zhicheng Zhao and Jun Wu
Appl. Sci. 2023, 13(20), 11374; https://doi.org/10.3390/app132011374 - 17 Oct 2023
Viewed by 793
Abstract
Remote sensing scene classification (RSSC) is a very crucial subtask of remote sensing image understanding. With the rapid development of convolutional neural networks (CNNs) in the field of natural images, great progress has been made in RSSC. Compared with natural images, labeled remote [...] Read more.
Remote sensing scene classification (RSSC) is a very crucial subtask of remote sensing image understanding. With the rapid development of convolutional neural networks (CNNs) in the field of natural images, great progress has been made in RSSC. Compared with natural images, labeled remote sensing images are more difficult to acquire, and typical RSSC datasets are consequently smaller than natural image datasets. Due to the small scale of these labeled datasets, training a network using only remote sensing scene datasets is very difficult. Most current approaches rely on a paradigm consisting of ImageNet pretraining followed by model fine-tuning on RSSC datasets. However, there are considerable dissimilarities between remote sensing images and natural images, and as a result, the current paradigm may present some problems for new studies. In this paper, to break free of this paradigm, we propose a general framework for scene classification (GFSC) that can help to train various network architectures on limited labeled remote sensing scene images. Extensive experiments show that ImageNet pretraining is not only unnecessary but may be one of the causes of the limited performance of RSSC models. Our study provides a solution that not only replaces the ImageNet pretraining paradigm but also further improves the baseline for RSSC. Our proposed framework can help various CNNs achieve state-of-the-art performance using only remote sensing images and endow the trained models with a stronger ability to extract discriminative features from complex remote sensing images. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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16 pages, 2947 KiB  
Article
Learning Spatial–Spectral-Dimensional-Transformation-Based Features for Hyperspectral Image Classification
by Jun Wu, Xinyi Sun, Lei Qu, Xilan Tian and Guangyu Yang
Appl. Sci. 2023, 13(14), 8451; https://doi.org/10.3390/app13148451 - 21 Jul 2023
Viewed by 735
Abstract
Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled information for non-central pixels of image patches. Therefore, it is challenging [...] Read more.
Recently, deep learning tools have made significant progress in hyperspectral image (HSI) classification. Most of existing methods implement a patch-based classification manner which may cause training test information leakage or waste labeled information for non-central pixels of image patches. Therefore, it is challenging to achieve remarkable classification performance via the traditional convolutional neural networks (CNN) in the absence of label information. Moreover, due to the limitation of convolutional kernel sizes and convolution operations, the spectral information of HSI cannot be fully utilized with a traditional CNN framework. In this paper, we implement pixel-based classification by a special data division strategy and propose a novel spatial–spectral dimensional transformation (SSDT) to obtain spectral features containing more spectral information. Then, we construct a fully convolutional network (FCN) with two branches based on 3D-FCN and 2D-FCN to achieve broader spatial and spectral information interaction. Finally, the fused features are utilized to realize accurate pixel-based classification. We verify our proposed method on three classic publicly available datasets; the overall classification accuracy and average accuracy reach 82.27%/87.85%, 83.81%/81.55%, and 85.97%/83.89%. Compared with the latest proposed method SS3FCN in the no-information-leakage scenario, the overall classification accuracy of our proposed method is improved by 1.72%, 4.95% and 0.2%, and the average accuracy is improved by 0.95%, 3.92% and 2.67% on the three databases, respectively. Experimental results demonstrate the effectiveness of the proposed SSDT and the proposed CNN framework. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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29 pages, 3771 KiB  
Article
Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
by Yi Zhang, Jie Ma, Xiaolin Qin, Yongming Li and Zuwei Zhang
Appl. Sci. 2023, 13(12), 7322; https://doi.org/10.3390/app13127322 - 20 Jun 2023
Viewed by 726
Abstract
Chronic diseases are severe and life-threatening, and their accurate early diagnosis is difficult. Machine-learning-based processes of data collected from the human body using wearable sensors are a valid method currently usable for diagnosis. However, it is difficult for wearable sensor systems to obtain [...] Read more.
Chronic diseases are severe and life-threatening, and their accurate early diagnosis is difficult. Machine-learning-based processes of data collected from the human body using wearable sensors are a valid method currently usable for diagnosis. However, it is difficult for wearable sensor systems to obtain high-quality and large amounts of data to meet the demands of diagnostic accuracy. Furthermore, existing feature-learning methods do not deal with this problem well. To address the above issues, a sample-pair envelope diamond autoencoder ensemble algorithm (SP_DFsaeLA) is proposed. The proposed algorithm has four main components. Firstly, sample-pair envelope manifold neighborhood concatenation mechanism (SP_EMNCM) is designed to find pairs of samples that are close to each other in a manifold neighborhood. Secondly, the feature-embedding stacked sparse autoencoder (FESSAE) is designed to extend features. Thirdly, a staged feature reduction mechanism is designed to reduce redundancy in the extended features. Fourthly, the sample-pair-based model and single-sample-based model are combined by weighted fusion. The proposed algorithm was experimentally validated on nine datasets and compared with the latest algorithm. The experimental results show that the algorithm is significantly better than existing representative algorithms and it achieves the highest improvement of 22.77%, 21.03%, 24.5%, 27.89%, and 10.65% on five criteria over the state-of-the-art methods. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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15 pages, 3634 KiB  
Article
The Fast Detection of Crop Disease Leaves Based on Single-Channel Gravitational Kernel Density Clustering
by Yifeng Ren, Qingyan Li and Zhe Liu
Appl. Sci. 2023, 13(2), 1172; https://doi.org/10.3390/app13021172 - 15 Jan 2023
Cited by 1 | Viewed by 1520
Abstract
Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision disease detection is [...] Read more.
Plant diseases and pests may seriously affect the yield of crops and even threaten the survival of human beings. The characteristics of plant diseases and insect pests are mainly reflected in the occurrence of lesions on crop leaves. Machine vision disease detection is of great significance for the early detection and prevention of plant diseases and insect pests. A fast detection method for lesions based on a single-channel gravitational kernel density clustering algorithm was designed to examine the complexity and ambiguity of diseased leaf images. Firstly, a polynomial was used to fit the R-channel feature histogram curve of a diseased leaf image in the RGB color space, and then the peak point and peak area of the fitted feature histogram curve were determined according to the derivative attribute. Secondly, the cluster numbers and the initial cluster center of the diseased leaf images were determined according to the peak area and peak point. Thirdly, according to the clustering center of the preliminarily determined diseased leaf images, the single-channel gravity kernel density clustering algorithm in this paper was used to achieve the rapid segmentation of the diseased leaf lesions. Finally, the experimental results showed that our method could segment the lesions quickly and accurately. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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16 pages, 1358 KiB  
Article
Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
by Xueping Su, Jiao He, Jie Ren and Jinye Peng
Appl. Sci. 2022, 12(20), 10226; https://doi.org/10.3390/app122010226 - 11 Oct 2022
Cited by 4 | Viewed by 2319
Abstract
Facing the massive tourism data, the recommendation system mines the user’s interest to provide a personalized information service. The Knowledge Graph is introduced into a recommendation system, as auxiliary information can effectively solve the problems about data sparse and cold-start. Therefore, this paper [...] Read more.
Facing the massive tourism data, the recommendation system mines the user’s interest to provide a personalized information service. The Knowledge Graph is introduced into a recommendation system, as auxiliary information can effectively solve the problems about data sparse and cold-start. Therefore, this paper proposes a new algorithm of personalized Chinese tourism recommendation based on the Knowledge Graph. First of all, because lack of the public Chinese tourism Knowledge Graph, a complete Chinese tourism Knowledge Graph is built. Secondly, a new B-TransD (Bernoulli-TransD) knowledge representation model is proposed to reduce the probability of false negative triples. Finally, the method of user interest model based on the attribute information of users and tourist attractions is proposed to improve the performance of the recommendation system. Experiments are conducted on a data set containing 9100 tourist attractions. The experimental results demonstrate that the proposed algorithm achieves significant improvement over the existing algorithms. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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18 pages, 3176 KiB  
Article
Mitigating Sensor and Acquisition Method-Dependence of Fingerprint Presentation Attack Detection Systems by Exploiting Data from Multiple Devices
by Marco Micheletto, Giulia Orrù, Roberto Casula and Gian Luca Marcialis
Appl. Sci. 2022, 12(19), 9941; https://doi.org/10.3390/app12199941 - 02 Oct 2022
Cited by 1 | Viewed by 1388
Abstract
The problem of interoperability is still open in fingerprint presentation attack detection (PAD) systems. This involves costs for designers and manufacturers who intend to change sensors of personal recognition systems or design multi-sensor systems, because they need to obtain sensor-specific spoofs and retrain [...] Read more.
The problem of interoperability is still open in fingerprint presentation attack detection (PAD) systems. This involves costs for designers and manufacturers who intend to change sensors of personal recognition systems or design multi-sensor systems, because they need to obtain sensor-specific spoofs and retrain the system. The solutions proposed in the state of the art to mitigate the problem still require data from the target sensor and are therefore not exempt from the problem of obtaining new data. In this paper, we provide insights for the design of PAD systems thanks to an overview of an interoperability analysis on modern systems: hand-crafted, deep-learning-based, and hybrid. We investigated realistic use cases to determine the pros and cons of training with data from multiple sensors compared to training with single sensor data, and drafted the main guidelines to follow for deciding the most convenient PAD design technique depending on the intended use of the fingerprint identification/authentication system. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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15 pages, 4055 KiB  
Article
Calligraphy Character Detection Based on Deep Convolutional Neural Network
by Xianlin Peng, Jian Kang, Yinjie Wu and Xiaoyi Feng
Appl. Sci. 2022, 12(19), 9488; https://doi.org/10.3390/app12199488 - 21 Sep 2022
Cited by 1 | Viewed by 1936
Abstract
Calligraphy (the special art of drawing characters with a brush specially made by the Chinese) is an integral part of Chinese culture, and detecting Chinese calligraphy characters is highly significant. At present, there are still some challenges in the detection of ancient calligraphy. [...] Read more.
Calligraphy (the special art of drawing characters with a brush specially made by the Chinese) is an integral part of Chinese culture, and detecting Chinese calligraphy characters is highly significant. At present, there are still some challenges in the detection of ancient calligraphy. In this paper, we are interested in the calligraphy character detection problem focusing on the calligraphy character boundary. We chose High-Resolution Net (HRNet) as the calligraphy character feature extraction backbone network to learn reliable high-resolution representations. Then, we used the scale prediction branch and the spatial information prediction branch to detect the calligraphy character region and categorize the calligraphy character and its boundaries. We used the channel attention mechanism and the feature fusion method to improve the detection effectiveness in this process. Finally, we pre-trained with a self-generated calligraphy database and fine-tuned with a real calligraphy database. We set up two groups of ablation studies for comparison, and the comparison results proved the superiority of our method. This paper found that the classification of characters and character boundaries has a certain auxiliary effect on single character detection. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Visual Signal Processing)
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