• IT-inspired divergences for shapes, images and graphs
  • MDL, Maximum Entropy, Minimax Entropy and other IT principles
  • Estimation of entropy and of other IT measures
  • Information projection
  • Information geometry
  • IT kernels and kernel machines
  • Integration with Bayesian/probabilistic models through IT
  • The role of IT in the integration of bottom-up/top-down processes
  • Alternative/complementary IT-like theories


  • Feature Extraction
  • Early Vision, Attention and Context
  • Image Segmentation
  • Image Parsing and Recognition
  • Image Registration
  • Object Recognition and Categorization
  • Cognitive Models for Perception
  • Pattern Recognition with Grammars and Graphs
  • Shape Matching and Learning
  • Analysis of High-Dimensional Data
  • Classifiers and I-Divergences
  • Classification and Compression
  • Active and on-Line Learning
  • Learning Graphical Models
  • Manifold Learning
  • Unsupervised and Semi-Supervised Learning
  • Clustering Ensembles and Consensus Methods
  • Feature Transformation and Selection
  • Quantification of Pattern Complexity
  • Natural Images Statistics
  • Domains: Biomedical Data, Remote Sensing, Multi-Media, 3D Shape Analysis, Robotics.