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Information Theory in Image Processing and Pattern Recognition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 4222

Special Issue Editors


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Guest Editor
Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto 15054-000, Brazil
Interests: pattern recognition; machine learning; image processing; computer graphics;

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Guest Editor
Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Uberlândia 38400-902, Brazil
Interests: medical image analysis; image processing; computer-aided diagnosis; multimedia; pattern recognition

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Guest Editor
Science and Technology Institute (ICT), Federal University of São Paulo (UNIFESP), São José dos Campos 04021-001, Brazil
Interests: medical image analysis; image processing; computer-aided diagnosis; multimedia; pattern recognition

Special Issue Information

Dear Colleagues,

Information theory (IT) explores techniques to quantify, store and communicate information.  The use of IT in image processing and pattern recognition covers theoretical and applied aspects involving the transmission, storage, and processing of information in different fields, such as computer science, biology, mathematics, chemistry, physics, engineering, medicine, and others. In this context, analysis based on entropy has made relevant advances, especially for the development of computer-aided detection (CADe) and computer-aided diagnosis (CADx), with new insights and approaches into the different processes of segmentation, feature extraction, feature selection, classification, representation learning, deep learning, learning deep features, and others. Thus, this Special Issue provides a forum for discussing challenging topics in information theory in image processing and pattern recognition, with new insights, theories, methods and approaches, and applications. Some issues of interest include, but are not limited to, the following:

  • IT in image processing and pattern recognition, considering CADe and CADx, with segmentation, texture analysis, feature analysis, classification and interpretation, exploring and applying the entropy concepts;
  • Multiscale and multidimensional approaches with entropy concepts;
  • Computer vision and machine learning devoted to CADe and CADx, exploring entropy issues in deep learning, representation learning, cooperative learning for multi-view analysis, learning deep features, and ensembles;
  • Analysis based on explainable artificial intelligence with entropy.

Prof. Dr. Leandro Alves Neves
Prof. Dr. Marcelo Zanchetta do Nascimento
Dr. Thaína Aparecida Azevedo Tosta
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. Entropy is an international peer-reviewed open access monthly 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

  • entropy and ensembles
  • multiscale and multidimensional concepts
  • image processing
  • feature extraction
  • machine learning
  • learning deep features
  • representation learning
  • cooperative learning
  • explainable artificial intelligence
  • computer-aided detection and diagnosis

Published Papers (3 papers)

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Research

26 pages, 7869 KiB  
Article
Classification of Multiple H&E Images via an Ensemble Computational Scheme
by Leonardo H. da Costa Longo, Guilherme F. Roberto, Thaína A. A. Tosta, Paulo R. de Faria, Adriano M. Loyola, Sérgio V. Cardoso, Adriano B. Silva, Marcelo Z. do Nascimento and Leandro A. Neves
Entropy 2024, 26(1), 34; https://doi.org/10.3390/e26010034 - 28 Dec 2023
Cited by 1 | Viewed by 1217
Abstract
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, [...] Read more.
In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of 94.83% to 100%, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images. Full article
(This article belongs to the Special Issue Information Theory in Image Processing and Pattern Recognition)
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23 pages, 2292 KiB  
Article
Multi-Channel Representation Learning Enhanced Unfolding Multi-Scale Compressed Sensing Network for High Quality Image Reconstruction
by Chunyan Zeng, Shiyan Xia, Zhifeng Wang and Xiangkui Wan
Entropy 2023, 25(12), 1579; https://doi.org/10.3390/e25121579 - 24 Nov 2023
Cited by 1 | Viewed by 831
Abstract
Deep Unfolding Networks (DUNs) serve as a predominant approach for Compressed Sensing (CS) reconstruction algorithms by harnessing optimization. However, a notable constraint within the DUN framework is the restriction to single-channel inputs and outputs at each stage during gradient descent computations. This constraint [...] Read more.
Deep Unfolding Networks (DUNs) serve as a predominant approach for Compressed Sensing (CS) reconstruction algorithms by harnessing optimization. However, a notable constraint within the DUN framework is the restriction to single-channel inputs and outputs at each stage during gradient descent computations. This constraint compels the feature maps of the proximal mapping module to undergo multi-channel to single-channel dimensionality reduction, resulting in limited feature characterization capabilities. Furthermore, most prevalent reconstruction networks rely on single-scale structures, neglecting the extraction of features from different scales, thereby impeding the overall reconstruction network’s performance. To address these limitations, this paper introduces a novel CS reconstruction network termed the Multi-channel and Multi-scale Unfolding Network (MMU-Net). MMU-Net embraces a multi-channel approach, featuring the incorporation of Adap-SKConv with an attention mechanism to facilitate the exchange of information between gradient terms and enhance the feature map’s characterization capacity. Moreover, a Multi-scale Block is introduced to extract multi-scale features, bolstering the network’s ability to characterize and reconstruct the images. Our study extensively evaluates MMU-Net’s performance across multiple benchmark datasets, including Urban100, Set11, BSD68, and the UC Merced Land Use Dataset, encompassing both natural and remote sensing images. The results of our study underscore the superior performance of MMU-Net in comparison to existing state-of-the-art CS methods. Full article
(This article belongs to the Special Issue Information Theory in Image Processing and Pattern Recognition)
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16 pages, 1731 KiB  
Article
Improved Recurrence Plots Compression Distance by Learning Parameter for Video Compression Quality
by Tatsumasa Murai and Hisashi Koga
Entropy 2023, 25(6), 953; https://doi.org/10.3390/e25060953 - 19 Jun 2023
Cited by 1 | Viewed by 1144
Abstract
As the Internet-of-Things is deployed widely, many time-series data are generated everyday. Thus, classifying time-series automatically has become important. Compression-based pattern recognition has attracted attention, because it can analyze various data universally with few model parameters. RPCD (Recurrent Plots Compression Distance) is known [...] Read more.
As the Internet-of-Things is deployed widely, many time-series data are generated everyday. Thus, classifying time-series automatically has become important. Compression-based pattern recognition has attracted attention, because it can analyze various data universally with few model parameters. RPCD (Recurrent Plots Compression Distance) is known as a compression-based time-series classification method. First, RPCD transforms time-series data into an image called “Recurrent Plots (RP)”. Then, the distance between two time-series data is determined as the dissimilarity between their RPs. Here, the dissimilarity between two images is computed from the file size, when an MPEG-1 encoder compresses the video, which serializes the two images in order. In this paper, by analyzing the RPCD, we give an important insight that the quality parameter for the MPEG-1 encoding that controls the resolution of compressed videos influences the classification performance very much. We also show that the optimal parameter value depends extremely on the dataset to be classified: Interestingly, the optimal value for one dataset can make the RPCD fall behind a naive random classifier for another dataset. Supported by these insights, we propose an improved version of RPCD named qRPCD, which searches the optimal parameter value by means of cross-validation. Experimentally, qRPCD works superiorly to the original RPCD by about 4% in terms of classification accuracy. Full article
(This article belongs to the Special Issue Information Theory in Image Processing and Pattern Recognition)
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