segmentTexture.m

function [ feature2DImage, Mask] = segmentTexture(I)
%SEGMENTTEXTURE Texture Segmentation Using Gabor Filters.
%
% Design an array of Gabor Filters which are tuned to different frequencies 
% and orientations. The set of frequencies and orientations is designed to 
% localize different, roughly orthogonal, subsets of frequency and orientation 
% information in the input image. Regularly sample orientations between [0,150] 
% degrees in steps of 30 degrees. Sample wavlength in increasing powers of 
% two starting from 4/sqrt(2) up to the hypotenuse length of the input image. 
% These combinations of frequency and orientation are taken from [Jain,1991] 
% cited in the introduction.
%
% Reference:  
% http://ch.mathworks.com/help/images/texture-segmentation-using-gabor-filters.html

imageSize = size(I);
numRows = imageSize(1);
numCols = imageSize(2);

wavelengthMin = 4/sqrt(2);
wavelengthMax = hypot(numRows,numCols);
n = floor(log2(wavelengthMax/wavelengthMin));
wavelength = 2.^(0:(n-2)) * wavelengthMin;

deltaTheta = 45;
orientation = 0:deltaTheta:(180-deltaTheta);

g = gabor(wavelength,orientation);

% Extract gabor magnitude features from source image. When working with Gabor 
% filters, it is common to work with the magnitude response of each filter. 
% Gabor magnitude response is also sometimes referred to as "Gabor Energy". 
% Each MxN Gabor magnitude output image in gabormag(:,:,ind) is the output 
% of the corresponding gabor filter g(ind).
gabormag = imgaborfilt(I,g);

% Post-process the Gabor Magnitude Images into Gabor Features. To use Gabor
% magnitude responses as features for use in classification, some
% post-processing is required. This post procesing includes Gaussian
% smoothing, adding additional spatial information to the feature set,
% reshaping our feature set to the form expected by the pca and kmeans
% functions, and normalizing the feature information to a common variance
% and mean. Each Gabor magnitude image contains some local variations, even
% within well segmented regions of constant texture. These local variations
% will throw off the segmentation. We can compensate for these variations
% using simple Gaussian low-pass filtering to smooth the Gabor magnitude
% information. We choose a sigma that is matched to the Gabor filter that
% extracted each feature. We introduce a smoothing term K that controls how
% much smoothing is applied to the Gabor magnitude responses.
for i = 1:length(g)
    sigma = 0.5*g(i).Wavelength;
    K = 3;
    gabormag(:,:,i) = imgaussfilt(gabormag(:,:,i),K*sigma);
end

% When constructing Gabor feature sets for classification, it is useful to
% add a map of spatial location information in both X and Y. This
% additional information allows the classifier to prefer groupings which
% are close together spatially.
X = 1:numCols;
Y = 1:numRows;
[X,Y] = meshgrid(X,Y);
featureSet = cat(3,gabormag,X);
featureSet = cat(3,featureSet,Y);

% Reshape data into a matrix X of the form expected by the kmeans function.
% Each pixel in the image grid is a separate datapoint, and each plane in
% the variable featureSet is a separate feature. In this example, there is
% a separate feature for each filter in the Gabor filter bank, plus two
% additional features from the spatial information that was added in the
% previous step. In total, there are 24 Gabor features and 2 spatial
% features for each pixel in our input image.
X = reshape(featureSet,numRows*numCols,[]);

% Normalize features to be zero mean, unit variance.
X = bsxfun(@minus, X, mean(X));
X = bsxfun(@rdivide,X,std(X));

% Visualize feature set. To get a sense of what the Gabor magnitude
% features look like, Principal Component Analysis can be used to move from
% a 26-D representation of each pixel in the input image into a 1-D
% intensity value for each pixel.
coeff = pca(X);
feature2DImage = reshape(X*coeff(:,1),numRows,numCols);

% Classify Gabor Texture Features using kmeans

% Repeat k-means clustering five times to avoid local minima when searching
% for means that minimize objective function. The only prior information
% assumed in this example is how many distinct regions of texture are
% present in the image being segmented. There are two distinct regions in
% this case. This part requires the Statistics and Machine Learning Toolbox.
L = kmeans(X,2,'Replicates',5);
L = reshape(L,[numRows numCols]);
Mask = L < 2;

% Examine the foreground and background images that result from the mask BW 
% that is associated with the label matrix L.
% Aseg1 = zeros(size(I),'like',I);
% Aseg2 = zeros(size(I),'like',I);
% BW = L == 0;
% BW = repmat(BW,[1 1]);
% Aseg1(BW) = I(BW);
% Aseg2(~BW) = I(~BW);

end