Visual learning related methods and concepts

A part of the CVonline computer vision resource summarizing some of the different methods for learning models, model parameters, or algorithmic parameters, as commonly used in computer vision and image processing.
  1. Observational learning
    1. Discrete observational learning
    2. Probabilistic observational learning
  2. Geometric feature learning
  3. Joint natural language and image data learning
    1. NLP learning techniques
  4. Learning technologies
    1. Bayesian learning/Probabilistic model learning
      1. Bayesian principal component analysis
      2. Latent variable learning
      3. Variational Bayesian methods
    2. Clustering
      1. Clustering coefficient
      2. Clustering quality evaluation (See also Generic methods->Model Selection Criteria)
        1. Dunn Clustering Index
        2. Silhouette Clustering Index
      3. Fuzzy clustering
      4. Hierarchical clustering
      5. k-means clustering
        1. Hierarchical k-means clustering
      6. Mean-shift clustering
      7. Neural gas clustering
      8. Parametric/Non-parametric clustering
      9. Pattern matrices
      10. Proximity matrices
      11. Self-organizing feature maps/Kohonen maps
      12. Superparamagnetism clustering
    3. Gaussian mixture models, Expectation-Maximization (EM)
    4. Ensemble learning
      1. Bootstrap aggregating
      2. Boosting
        1. AdaBoost
        2. DenseBoost
        3. TextonBoost
      3. Extremely random trees (Extra-trees)
      4. Random forests
      5. Vector boosting
    5. Feature selection
    6. Gaussian process learning and classification
    7. Genetic programming/Genetic algorithms
    8. Neural networks
    9. Principal component analysis
    10. Support vector machines
      1. Exemplar SVM
      2. Kernel methods
      3. Kernel trick
      4. Structured SVM
      5. Relevance vector machine
    11. Semi-supervised learning
    12. Vector quantization
  5. Shape model learning
    1. Range data fusion
    2. Space carving
    3. Structured learning
      1. Architectural models
    4. Volumetric model recovery
    5. Voxel coloring
      1. Marching cubes
  6. Property learning
    1. Spatio-temporal patterns
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