Feature extraction plays a key role in pattern recognition. Here we present a rotation-invariant feature extraction methodology based on a structural approach using co-occurrence statistics.

Graphical abstract of Structural Co-occurrence Matrix (SCM) used for feature extraction


  • provided SCM source code can be used to analyze 1-D signals, and n-dimensional data with code adaptations.

Rotation-invariant Feature Extraction using a Structural Co-occurrence Matrix was published in Measurement v.94, p.406-415, dec/2016 – Elsevier. Database and source code can be used if properly cited.

We assessed the performance of our method comparing it to a gray-level co-occurrence matrix, local binary patterns and invariant moments in pattern recognition experiments. The results show that the proposed method provides an efficient and fast way to analyze digital images.

  • Read this document for further information and examples (only in portuguese).


  • A structural approach for image analysis based on co-occurrence statistics.
  • Examples of how to set the parameters and how to use the proposed methodology.
  • A set of attributes to describe the low-level image structures.
  • A comparison of our method and three rotation-invariant feature extraction methods.
  • Evaluation of our method applied to image classification on different databases.

The files used to evaluate the proposed methods are as following:


Feature extraction methods

Machine learning algorithms

Matlab code to generate the results