Image and Video Processing and Retrieval Based on Machine Learning and Deep Learning

Dear Colleagues,

Image and video processing and retrieval using machine learning and deep learning constitutes a rapidly growing field of research with numerous applications in areas such as multimedia search, video surveillance, medical imaging, content-based retrieval, and computer vision.

Deep learning methods, including convolutional neural networks (CNNs), autoencoders, transformers, and generative adversarial networks (GANs) have proven to be effective in image and video processing and retrieval tasks. These techniques can recognize, classify, and generate visual content with high levels of accuracy, leading to the development of powerful algorithms.

Machine learning and deep learning techniques are useful in image and video processing applications such as image and video enhancement, restoration, segmentation, object detection and recognition, image and video classification, and content-based image and video retrieval. They have significant potential for use to improve and automate many aspects of image and video processing, making these important research areas with numerous practical applications.

Image and video retrieval involves the automatic searching and categorization of large collections of visual media. Machine learning and deep learning algorithms have made significant strides in this area, enabling the efficient and effective content-based retrieval of images and videos. As research continues in this area, the development of new algorithms and techniques is expected to result in more accurate, efficient, and reliable processing and retrieval systems for visual media, revolutionizing the way we interact with and analyze visual content.

This Special Issue of Electronics, entitled “Image and Video Processing and Retrieval Based on Machine Learning and Deep Learning”, aims to present recent research work on image processing, video processing, as well on retrieval applications. We invite scholars to submit original research papers with novel findings as well as review articles describing the current state of the art and future perspectives in this field.

Dr. Raju Shrestha
Prof. Dr. Sule Yildirim Yayilgan
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.

• imaging
• image processing
• video processing
• retrieval
• machine learning
• deep learning