SI: Combinatorial Optimization in Imaging Sciences


Combinatorial optimization models and solution techniques are widely used in various facets of imaging sciences, including topological image analysis, image segmentation, registrations, restoration, classification and clustering, stereo matching, optical flows, and image filtering, among others. Optimization issues also occur in the topological analysis of digitized images and scenes. In recent years, structural results and methods for processing very large and complex images are of increasing importance. Commonly, related research combines approaches and techniques from combinatorial optimization, machine learning, and artificial intelligence.

 The main objective of this special issue is to stimulate researchers to advance the theory of combinatorial optimization and its implementation in developing novel machine intelligence for imaging sciences. Authors are expected to propose original ideas and develop new techniques, structural results, and algorithms that improve researchers’ ability to solve practical problems.


While a work presenting combinatorial optimization results of exceptional quality and impact would be relevant to the special issue, the expected focus of the issue is the study of combinatorial optimization problems directly or potentially applicable to imaging sciences, including image analysis and processing, computer graphics, computer vision, and visualization. Data analytics approaches are of particular interest. In this regard, specific topics of interest include (but are not limited to):

  • Optimization problems in discrete geometry for computer imagery
  • Combinatorial optimization problems on digital manifolds
  • Integer programming models and solutions
  • Graph formulations and algorithms for imaging problems 
  • Extremal combinatorial properties of digitized sets 
  • Combinatorial optimization for topological image analysis
  • Combinatorial algorithms for processing very large digital images
  • Compressed image representation and parallel algorithms for processing massive data sets
  • Artificial intelligence and optimization 
  • Neural networks and machine learning for image analysis and processing.


Papers will be evaluated based on their mathematical depth, presentation quality, and relevance to the theme of the special issue. The submitted papers must be clearly written in excellent English and must present original research that is not currently under review elsewhere. Manuscripts based on conference papers must contain a substantial amount (at least 35-40%) of essentially new material. A successful theoretical paper would present significant contributions to the theory of combinatorial optimization and a discussion on possible applications to imaging. A successful application-driven paper should employ solid mathematical theory and experimentation supporting the results. Submissions must conform to the layout, format and page limit provided in the guidelines for authors.


To be completed according to the instructions from Springer. Sample text:

All manuscripts should be submitted through the Springer’s Editorial Manager of Journal of Combinatorial Optimization. The authors must select the article type “SI: COIS” at the “Article Type” step of the submission process. Detailed submission guidelines are available in the Submission Guidelines at:


  • Submission opening: August 1, 2020
  • Submission deadline: October 15, 2020
  • First round notification: December 15, 2020
  • Revised version due: January 15, 2021
  • Second round notification: March 15, 2021
  • Revised version due: April 15, 2021
  • Final notification: April 30, 2021
  • Special issue submitted to Editor-in Chief: May 15, 2021


Reneta Barneva, managing guest-editor
Department of Applied Professional Studies
School of Business
SUNY at Fredonia, Fredonia, USA

Tibor Lukić 
Department of Mathematic
Faculty of Technical Sciences
University of Novi Sad, Novi Sad, Serbia

For all information in one page kindly download the PDF below:

JOCO - Call for Papers - Special Issue Imaging Science (pdf, 73.86 kB)