Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Algorithms for Content Based Image Retrieval
share announcement

Algorithms for Content Based Image Retrieval

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 October 2019) | Viewed by 7341

Special Issue Editor

Prof. Dr. Denis Sidorov

E-Mail Website
Guest Editor
Russian Academy of Sciences, Melentiev Energy Systems Institute, 664033 Irkutsk, Russia
Interests: nverse problems; integral equations; machine learning; nonlinear systems; bifurcation; numerical methods; energy systems engineering; optimal design and operation; forecasting; energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous growth of massive imaging data warehouses in various fields including geophysical research, NDT, medical diagnostics, and homeland security has led to the development of various techniques for content-based image retrieval; this has also been an active research field in the last twenty years. However, image retrieval under conditions of rapid accumulation of gigabytes of imaging data remains one of the main challenges in computer science. The analysis and retrieval of such massive imaging data warehouses has paved the way for various novel mathematical model developments, with applications ranging from radiology to UAV navigation. To this end, a variety of technological and mathematical questions need to be addressed, from data preprocessing, restoration, and multi-physics image sensing to features engineering and classification. 

The purpose of this Special Issue is to bring together applied mathematicians, software engineers, and experts from industry to meet and share their experiences in order to build future robust content-based image retrieval systems.

Prof. Dr. Denis N. Sidorov
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. Algorithms 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

  • Feature engineering
  • Computer vision
  • Machine learning
  • Optimization
  • Image indexing.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 1286 KiB  
Article
Fingerprints Classification through Image Analysis and Machine Learning Method
by Huong Thu Nguyen and Long The Nguyen
Algorithms 2019, 12(11), 241; https://doi.org/10.3390/a12110241 - 11 Nov 2019
Cited by 23 | Viewed by 6819
Abstract
The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the [...] Read more.
The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases. Full article
(This article belongs to the Special Issue Algorithms for Content Based Image Retrieval)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop