Documentation>C API
Vision Lab Features Library (VLFeat)

Version
  0.9.21

Author
  The VLFeat Team

Copyright © 2012-14,18 The VLFeat Authors

Copyright © 2007-11 Andrea Vedaldi and Brian Fulkerson

The VLFeat C library implements common computer vision algorithms, with a special focus on visual features, as used in state-of-the-art object recognition and image matching applications.

VLFeat strives to be clutter-free, simple, portable, and well documented.

# Contents

- Visual feature detectors and descriptors
  - sift
  - dsift
  - Maximally Stable Extremal Regions (MSER)
  - Covariant feature detectors
  - Gaussian Scale Space (GSS)
  - Histogram of Oriented Gradients (HOG) features
  - Fisher Vector encoding (FV)
  - Vector of Locally Aggregated Descriptors (VLAD) encoding
  - Local Intensity Order Pattern (LIOP) descriptor
  - Local Binary Patterns (LBP) descriptor
- Clustering and indexing
  - K-means clustering
  - Integer K-means (IKM)
  - Hierarchical Integer K-means (HIKM)
  - Gaussian Mixture Models (GMM)
  - Agglomerative Information Bottleneck (AIB)
  - KD-trees and forests
- Segmentation
  - Simple Linear Iterative Clustering (SLIC)
  - Quick shift image segmentation
- Statistical methods
  - Agglomerative Information Bottleneck (AIB)
  - Homogeneous kernel map
  - Support Vector Machines (SVM)
- Utilities
  - Random number generator
  - Mathematical operations
  - String operations
  - Image operations
  - PGM image format
  - Generic heap object (priority queue)
  - Rodrigues formula
  - MATLAB MEX helper functions
- General information
  - Conventions
  - General support functionalities
  - Portability features
  - Memory and resource handling
  - Objects
  - Threading
  - MATLAB integration
  - Preprocessor metaprogramming
- Developing the library
- Glossary