Table of Contents Author Guidelines Submit a Manuscript

Recent Machine Learning Progress in Image Analysis and Understanding

Call for Papers

Recently, artificial intelligence and machine learning have attracted increasing attention and achieved great success in both research community and industry especially in the field of multimedia. With the recent progress in machine learning especially in deep learning, many tasks in image analysis and understanding have been applied to solve real problems. For example, since the deep learning based classifier was successfully used in image classification in 2012, deep learning has also been widely used in other computer vision tasks such as video classification and image super-resolution. Learning an effective feature representation from a large number of data is capable of extracting the underlying structure features of the data, which produce better representation than hand-crafted features since the learned features adapt well to the tasks at hand. However, most of the existing deep learning based methods need to learn a huge number of parameters especially with the increasingly complicated network, which restricts their applications in image analysis and understanding in real-time environments.

The primary purpose of this special issue is to organize a collection of recently developed machine learning methods as well as their applications in image analysis and understanding. The special issue is intended to be an international forum for researchers to report the recent developments in this field in an original research paper style. Review articles which describe the current state of the art are also welcomed.

Potential topics include but are not limited to the following:

  • Image classification
  • Image segmentation
  • Image tracking
  • Image saliency
  • Behavior understanding

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 22 June 2018
Publication DateNovember 2018

Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

Guest Editors

  • Huiyu Zhou, Queen’s University Belfast, Belfast, UK
  • Lei Zhang, University of Pittsburgh, Pittsburgh, USA