Computer vision feature extraction toolbox

Computer vision feature extraction toolbox for image classification

The goal of this toolbox is to simplify the process of feature extraction, of commonly used computer vision features such as HOG, SIFT, GIST and Color, for tasks related to image classification. The details of the included features are available in FEATURES.md.

In addition to providing some of the popular features, the toolbox has been designed for use with the ever increasing size of modern datasets - the processing is done in batches and is fully parallelized on a single machine (using parfor), and can be easily distributed across multiple machines with a common file system (the standard cluster setup in many universities).

The features extracted in a bag-of-words manner ('color', 'hog2x2', 'hog3x3', 'sift', 'ssim') are encoded using Locality-Constrained Linear Coding to allow the use of a linear classifier for fast training + testing.

In my experients, I have found 'hog2x2' or 'hog3x3' to be most effective as global image features, and tend to perform even better when combined with 'color' features which contain complementary information.

The toolbox works on Matlab and Octave. Octave may still have some compatibility issues though and doesn't support paralell processing.

Installation instructions and sample code are available in README.md.