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Algorithms
Special Issues
Machine Learning in Image and Video Processing
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Machine Learning in Image and Video Processing

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

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 4284

Special Issue Editor

Dr. Baiyuan Ding

E-Mail Website
Guest Editor
Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China
Interests: synthetic aperture radar; target recognition; feature extraction

Special Issue Information

Dear Colleagues,

Given the exponentially increasing amount of data from cameras, webcams, or other optical or radar sensors, a huge number of images or videos of differing natures can be used for different applications. In particular, the problem of high-performance target recognition, anomaly detection, etc. plays an important role in both military and civil fields. In the military field, image processing can be used for intelligence interpretation and battlefield surveillance. In the civil field, image or video processing can be used for face recognition, driver assistance systems, geological survey, etc. However, to the problem of achieving high performance for image classification, target detection and recognition, video tracking, etc. is still challenging because of the complex situation in real-world scenarios, including noises, occlusions, deformations, etc. Recently, progress in the machine learning computer vision domain has highlighted its potential in practical applications. As a typical example, deep neural network methodologies have shown their great predominance in image and video processing, including segmentation, classification, recognition, etc. Therefore, the aim of this Special Issue is to apply advanced machine learning approaches in image and video processing. The Issue will provide novel guidance for machine learning researchers and broaden the perspectives of machine learning and computer vision researchers. Original research and review articles are welcomed. Potential topics include, but are not limited to the following: scientific programming for image and video processing; scientific programming tools in machine learning; artificial intelligence in image interpretation; artificial intelligence in video interpretation; deep learning in image classification/target recognition; and deep learning in video detection/tracking.

Dr. Baiyuan Ding
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

  • machine learning
  • deep learning
  • image processing
  • video processing

Published Papers (2 papers)

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Research

14 pages, 1186 KiB  
Article
Is One Teacher Model Enough to Transfer Knowledge to a Student Model?
by Nicola Landro, Ignazio Gallo and Riccardo La Grassa
Algorithms 2021, 14(11), 334; https://doi.org/10.3390/a14110334 - 15 Nov 2021
Cited by 1 | Viewed by 1539
Abstract
Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve [...] Read more.
Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve better performance. In this paper, we designed a transfer learning methodology that combines the learned features of different teachers to a student network in an end-to-end model, improving the performance of the student network in classification tasks over different datasets. In addition to this, we tried to answer the following questions which are in any case directly related to the transfer learning problem addressed here. Is it possible to improve the performance of a small neural network by using the knowledge gained from a more powerful neural network? Can a deep neural network outperform the teacher using transfer learning? Experimental results suggest that neural networks can transfer their learning to student networks using our proposed architecture, designed to bring to light a new interesting approach for transfer learning techniques. Finally, we provide details of the code and the experimental settings. Full article
(This article belongs to the Special Issue Machine Learning in Image and Video Processing)
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20 pages, 9766 KiB  
Article
Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
by Yong Mei, Hao Chen and Shuting Yang
Algorithms 2021, 14(10), 300; https://doi.org/10.3390/a14100300 - 19 Oct 2021
Viewed by 1683
Abstract
High-resolution remote sensing image building target detection has wide application value in the fields of land planning, geographic monitoring, smart cities and other fields. However, due to the complex background of remote sensing imagery, some detailed features of building targets are less distinguishable [...] Read more.
High-resolution remote sensing image building target detection has wide application value in the fields of land planning, geographic monitoring, smart cities and other fields. However, due to the complex background of remote sensing imagery, some detailed features of building targets are less distinguishable from the background. When carrying out the detection task, it is prone to problems such as distortion and the missing of the building outline. To address this challenge, we developed a novel building target detection method. First, a building detection method based on rectangular approximation and region growth was proposed, and a saliency detection model based on the foreground compactness and local contrast of manifold ranking is used to obtain the saliency map of the building region. Then, the boundary prior saliency detection method based on the improved manifold ranking algorithm was proposed for the target area of buildings with low contrast with the background in remote sensing imagery. Finally, fusing the results of the rectangular approximation-based and saliency-based detection, the proposed fusion method improved the Recall and F1 value of building detection, indicating that this paper provides an effective and efficient high-resolution remote sensing image building target detection method. Full article
(This article belongs to the Special Issue Machine Learning in Image and Video Processing)
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