Coresets for dictionary learning

The following code implements the coreset for dictionary learning algorithm as it appears in

1. Feldman D., Feigin M. and Sochen N., “Learning Big (Image) Data via Coresets for Dictionaries”, in Journal of Mathematical Imaging and Vision, March 2013
2. Feigin M., Feldman D. and Sochen N., “From High Definition Image to Low Space Optimization”, in Scale Space and Variational Methods (SSVM), Ein-Gedi, Israel, 2011

The code for building a Coreset for KSVD dictionary learning is included in the following two files:

1. KSVD Coreset code
2. SVD Coreset code

Note that the second file is only needed if a non constant first approximation is requested.

Two auxiliary functions that may be of use, decompose an image into a patches and build the vector matrix, and re-composition of the image. Note that these version do not create overlapping patches, which are required for some algorithms (mainly denoising) that use KSVD.

1. Decompose image into patch vectors
2. Rebuild image from vectors matrix

The code itself is easy to use.

To compute a dictionary using KSVD the KSVD-Box and OMP-Box code by Ron Rubnstein is needed and can be downloaded from here.

Next, the easiest approach is to just compute the actual dictionary by:

1. Create a new KSVDCoresetAlg object

    K = KSVDCoresetAlg;

2. Change the default settings for K.sampleSize and K.svdVecs if needed
3. Decompose images into patches to get the input to the dictionary learning code (default is 8 X 8 patches)

    P = BuildY(img);

4. Compute the dictionary (note that the input vectors are given in the rows of P).

    D = K.computeDictionary(P, sparsity, dictsize, KSVD_iter_num);

5. Compute the reconstruction coefficients for the dictionary

   X = omp(D, P, D'*D, sparsity);

6. Reconstruct patches from dictionary and coefficients

   RP = D*X;

7. Reconstruct the image from the reconstructed patches (assumes default block size)

   P = YToImage(RP, size(img));

The Coreset may be obtained directly by using the K.computeCoreset function, but note the implementation in K.computeDictionary as to the correct usage of the approximated dictionary (variable tD in K.computeDictionary)