torfsen / local_binary_patterns

Texture Classification using Local Binary Patterns

Texture Classification using Local Binary Patterns

The task of automatically assigning one of several categories to a texture (i.e. an image of a pattern) is called texture classification. For example, one might want to classify a photo of a tree bark with the corresponding species' names. In machine learning, this problem is solved by learning the classification from a set of samples (the training data). After learning, the algorithm can then (try to) classify previously unseen textures.

One of the many possible techniques for doing this has been proposed by Ojala et al. in their heavily cited paper

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns (IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, July 2002).

This is a Java implementation of that approach. Note that while fully functional, the implementation is not intended for large-scale use. Typical input sizes are 32x32 pixels.

See also an implementation for MATLAB from the University of Oulu and one in Python from Luis Pedro Coelho.

local_binary_patterns's Issues

Would you please implement and include the histogram refinement explained in https://github.com/Ashwani21/Local-texture-descriptors in this repo.

Histogram refinement results in https://github.com/Ashwani21/Local-texture-descriptors are very promising.