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Advanced Theories and Applications of Multimedia Information Technology (Invited Papers...
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Advanced Theories and Applications of Multimedia Information Technology (Invited Papers from MITA 2023)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 2484

Special Issue Editors

Prof. Dr. Byung-Gyu Kim

E-Mail Website
Guest Editor
Division of AI Engineering, Sookmyung Women's University, Seoul 04310, Republic of Korea
Interests: deep learning; multimedia processing; visual intelligence; emotion recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Platoš

E-Mail Website
Guest Editor
Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
Interests: machine learning; data compression; data mining; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multimedia-based information system is an essential part of the various human-centric interaction services. Multimedia data are a super unit of data in which various individual data are synchronized,  and can provide richer information, such as user-friendly information. Recently, the development of artificial intelligence technology has reached the level of providing necessary information or services by judging human emotion in the multimedia field. Although the application of artificial intelligence technology such as deep learning is becoming common in the field of research on individual media signals such as video, audio, and text, independent intelligence in a simple combination of these individual media signals has limitations in generating sufficient information and service technologies.

MITA 2023 is an important international academic conference organized by the Korea Multimedia Society (KMMS) on the theme of multimedia information systems. Through this international conference, we have provided an academic discussion space to fuse multimedia data and artificial intelligence technology, exchange research results to be used in industrial IOT and media services, and share the latest algorithms and development results for new artificial intelligence convergence technologies.

In this Special Issue, various AI convergence studies based on multimedia information processing algorithms and technologies, trends, and directions of the latest technologies are presented. In addition, research results and information are provided to researchers in the field of multimedia convergence technology.

Prof. Dr. Byung-Gyu Kim
Dr. Jan Platoš
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. Electronics 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

  • multimedia information system
  • Artificial intelligence
  • deep learning
  • industrial IoT
  • smart media
  • cross media optimization

Published Papers (2 papers)

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Research

12 pages, 2697 KiB  
Article
FontFusionGAN: Refinement of Handwritten Fonts by Font Fusion
by Avinash Kumar, Kyeolhee Kang, Ammar ul Hassan Muhammad and Jaeyoung Choi
Electronics 2023, 12(20), 4246; https://doi.org/10.3390/electronics12204246 - 13 Oct 2023
Viewed by 1154
Abstract
Handwritten fonts possess unique expressive qualities; however, their clarity often suffers because of inconsistent handwriting. This study introduces FontFusionGAN (FFGAN), a novel method that enhances handwritten fonts by mixing them with printed fonts. The proposed approach leverages a generative adversarial network (GAN) to [...] Read more.
Handwritten fonts possess unique expressive qualities; however, their clarity often suffers because of inconsistent handwriting. This study introduces FontFusionGAN (FFGAN), a novel method that enhances handwritten fonts by mixing them with printed fonts. The proposed approach leverages a generative adversarial network (GAN) to synthesize fonts that combine the desirable features of both handwritten and printed font styles. Training a GAN on a comprehensive dataset of handwritten and printed fonts enables it to produce legible and visually appealing font samples. The methodology was applied to a dataset of handwriting fonts, showing substantial enhancements in the legibility of the original fonts, while retaining their unique aesthetic essence. Unlike the original GAN setting where a single noise vector is used to generate a sample image, we randomly selected two noise vectors, z1 and z2, from a Gaussian distribution to train the generator. Simultaneously, we input a real image into the fusion encoder for exact reconstruction. This technique ensured the learning of style mixing during training. During inference, we provided the encoder with two font images, one handwritten and the other printed font, to obtain their respective latent vectors. Subsequently, the latent vector of the handwritten font image was injected into the first five layers of the generator, whereas the latent vector of the printed font image was injected into the last two layers to obtain a refined handwritten font image. The proposed method has the potential to improve the readability of handwritten fonts, offering benefits across diverse applications, such as document composition, letter writing, and assisting individuals with reading and writing difficulties. Full article
(This article belongs to the Special Issue Advanced Theories and Applications of Multimedia Information Technology (Invited Papers from MITA 2023))
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12 pages, 1541 KiB  
Article
Stabilized Temporal 3D Face Alignment Using Landmark Displacement Learning
by Seongmin Lee, Hyunse Yoon, Sohyun Park, Sanghoon Lee and Jiwoo Kang
Electronics 2023, 12(17), 3735; https://doi.org/10.3390/electronics12173735 - 04 Sep 2023
Viewed by 1016
Abstract
One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome [...] Read more.
One of the most crucial aspects of 3D facial models is facial reconstruction. However, it is unclear if face shape distortion is caused by identity or expression when the 3D morphable model (3DMM) is fitted into largely expressive faces. In order to overcome the problem, we introduce neural networks to reconstruct stable and precise faces in time. The reconstruction network extracts the 3DMM parameters from video sequences to represent 3D faces in time. Meanwhile, our displacement networks learn the changes in facial landmarks. In particular, the networks learn changes caused by facial identity, facial expression, and temporal cues, respectively. The proposed facial alignment network exhibits reliable and precise performance in reconstructing static and dynamic faces by leveraging these displacement networks. The 300 Videos in the Wild (300VW) dataset is utilized for qualitative and quantitative evaluations to confirm the effectiveness of our method. The results demonstrate the considerable advantages of our method in reconstructing 3D faces from video sequences. Full article
(This article belongs to the Special Issue Advanced Theories and Applications of Multimedia Information Technology (Invited Papers from MITA 2023))
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