Special Session: Evolutionary Computation for Computer Vision and Image Analysis

Computer vision is a big research field that studies to use computers process, extract, analyze and understand information from digital images and videos as the human vision system does. It covers a wide range of applications in many important fields, including engineering, biology, medicine, remote sensing, and business. Furthermore, many computer vision tasks are highly related to our daily life, from face detection in the mobile phone to self-driving vehicles. Typical tasks related to computer vision and image analysis include image processing, edge detection, image classification, image segmentation, object detection, scene analysis, biological identification, motion analysis, and image restoration. These tasks have not been comprehensively solved, particularly in the era of big data, when image data are easy to obtained but may be more challenging to analyze. It is necessary to develop new effective and efficient methods to solve the tasks in computer vision.

Evolutionary computation is a sub-field of artificial intelligence that includes a family of nature-inspired population-based algorithms/techniques. Evolutionary computation techniques have promising global search/optimization ability to find high-quality solutions to complex problems without requiring rich domain knowledge. Existing evolutionary computation paradigms include Genetic algorithms (GAs), Genetic programming (GP), Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS), Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential Evolution (DE), Evolutionary Multi-objective Optimization (EMO) and Memetic Computing (MC). Evolutionary computation techniques have been successfully applied to solve many computer vision and image analysis tasks, including image classification, image segmentation, object recognition, and image registration. However, the potential of evolutionary computation has not been comprehensively investigated in computer vision and image analysis. The challenges of improving effectiveness, efficiency, and interpretability, and reducing the requirement of domain knowledge and human intervention are urging to be addressed by investigating new evolutionary computation approaches to computer vision and image analysis.

This special session aims to investigate the use of evolutionary computation for computer vision and image analysis, covering ALL different evolutionary computation paradigms and their applications to computer vision and image analysis. It will bring together researchers and practitioners from around the world to discuss the latest advances in the field and will act as a major forum for the presentation of recent research. Authors are invited to submit their original and unpublished work to this special session. Topics related to all aspects of evolutionary computation for computer vision and image analysis, such as theories, algorithms, systems and applications, are welcome.

Topics of interest include but are not limited to:

  • Evolutionary algorithms
  • Swarm intelligence
  • Evolutionary multi-objective optimization
  • Genetic algorithms
  • Genetic programming
  • Particle swarm optimisation
  • Ant colony optimisation
  • Differential evolution
  • Evolutionary transfer learning
  • Evolutionary multitask learning
  • Evolutionary machine learning
  • Evolving neural networks
  • Image processing
  • Image classification
  • Image segmentation
  • Edge detection
  • Image restoration
  • Image registration
  • Object detection
  • Object recognition
  • Image retrieval
  • Texture analysis
  • Scene analysis and understanding
  • Face recognition
  • Facial expression and emotion analysis
  • Biological identification
  • Action recognition and human activity analysis 
  • Medical image analysis
  • Remote sensing image analysis
  • Feature extraction and analysis
  • Representation learning
  • Pattern recognition
  • Big data in computer vision and image analysis
  • Small data in computer vision and image analysis
  • Other computer vision and image analysis applications 
  • Other evolutionary computation paradigms

Organizers: Ying Bing, Bing Xue and Mengjie Zhang