Computer Analysis of Images and Patterns for Large-Scale Multimedia Database Management

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 10716

Special Issue Editor

Computer science department, Faculty of Engineering, University of Mons, 7000 Mons, Belgium
Interests: image analysis; multimedia retrieval; large-scale database analysis; deep learning

Special Issue Information

Dear Colleagues,

Recent years have witnessed tremendous advances in image and pattern analysis systems. These systems are directly related to various practical applications, including object detection and screening, video surveillance, product or pattern recognition, and medical applications. These kinds of multimedia applications contain an extremely high volume of multimedia data (2D and 3D images, sound, video). That is why we need to develop highly efficient algorithms for analyzing large datasets of images. Another challenge in this era is achieving clever and quick access to these huge datasets in order to access data in a reasonable time. In this context, large-scale image retrieval is a fundamental task. Many methods have been developed in the literature to achieve fast and efficient navigation in large databases using the famous content-based image retrieval (CBIR) methods and dimensionality reduction techniques, for example. More recently, methods based on convolutional neural networks (CNNs) for feature extraction and image classification have been widely used. The performances of all these methods may encounter some challenging conditions observed in large-scale datasets, such as the size of feature vectors, computing time, multimodal queries, partial search, occlusion, clutter, and variations in illumination and viewpoint. 

This Special Issue will focus on recent advances in large-scale image retrieval, 3D database management, video indexing and retrieval, deep learning methods for images analysis, deep learning for mulimedia retrieval, dimentionamlity reduction for multimedia retrieval, and large-scale medical database management. This Special Issue on “Large-Scale Multimedia Database Management” aims at bringing together the latest research, and development efforts to promote new theories, techniques, and methods allowing to exploit huge multimedia databases. Potential topics include but are not limited to the following: 

  • Large-scale image retrieval system;
  • Deep learning for multimedia retrieval;
  • Multimodal queries for multimedia retrieval;
  • 3D database management;
  • 3D vision;
  • Biomedical images and pattern analysis;
  • Artificial intelligence and computer vision for image analysis;
  • Data analysis and visualization;
  • Multimedia medical imaging;
  • High-performance computing for multimedia retrieval;
  • Face and gesture analysis;
  • Feature extraction in large-scale databases;
  • Machine learning for image and pattern analysis;
  • Edge computing and multimedia analysis;
  • Motion and tracking;
  • Object recognition;
  • Mobile multimedia analysis;
  • Image segmentation.
Prof. Dr. Saïd Mahmoudi
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. Information 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 1600 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

  • Image analysis
  • Image retrieval
  • Large scale databases
  • Deep learning
  • Segmentation
  • Video analysis

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

4 pages, 363 KiB  
Editorial
Large Scale Multimedia Management: Recent Challenges
by Saïd Mahmoudi and Mohammed Amin Belarbi
Information 2022, 13(1), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/info13010028 - 10 Jan 2022
Cited by 2 | Viewed by 1328
Abstract
In recent years, we have witnessed an incredible and rapid growth of multimedia content in its different forms (2D and 3D images, text, sound, video, etc [...] Full article

Research

Jump to: Editorial

13 pages, 4205 KiB  
Article
A New Edge Computing Architecture for IoT and Multimedia Data Management
by Olivier Debauche, Saïd Mahmoudi and Adriano Guttadauria
Information 2022, 13(2), 89; https://0-doi-org.brum.beds.ac.uk/10.3390/info13020089 - 14 Feb 2022
Cited by 18 | Viewed by 6363
Abstract
The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we [...] Read more.
The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT). Full article
Show Figures

Figure 1

24 pages, 3359 KiB  
Article
Contour Extraction Based on Adaptive Thresholding in Sonar Images
by Antonios Andreatos and Apostolos Leros
Information 2021, 12(9), 354; https://0-doi-org.brum.beds.ac.uk/10.3390/info12090354 - 30 Aug 2021
Cited by 2 | Viewed by 2252
Abstract
A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm with two alternative histogram approximation methods is presented. Histogram [...] Read more.
A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm with two alternative histogram approximation methods is presented. Histogram approximation is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar images. The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases. Full article
Show Figures

Figure 1

Back to TopTop