Compute histogram of shape contexts (assuming that SC has already been calculated). Written for the stupid professor, who keeps failing at the FizzBuzz test. It may contain bugs, as most code written for the academic papers is really buggy.

generateTest.m

% Generates test data

nPoints = 20;
nBins = 6;
nImages = 72;

shapeContexts = cell(nImages, 1);
for i = 1:nImages
	sc = zeros(nPoints, nBins);
	for j = 1:nPoints
		sc(j, :) = histc(randi(nBins, [1, nPoints]), 1:nBins);
	end
	shapeContexts{i} = sc;
end

shapemeQuantize.m

function [bow] = shapemeQuantize(shapeContexts, dCodebook, nSamplings)
% shapeContexts: nImages-by-1 cell array, 
%     each element of which is an nPoints-by-nBin array;

	nImages = size(shapeContexts, 1);
	if ~exist('nSamplings', 'var')
		nSamplings = min(600, nImages);
	end

	[nPoints, nBins] = size(shapeContexts{1});

	% Preparing
	SCSampled = zeros(nSamplings * nPoints, nBins);

	idx = sort(randsample(nImages, nSamplings))';
	indices = (1:nPoints)';
	for i = idx
		SCSampled(indices, :) = shapeContexts{i};
		indices = indices + nPoints;
	end

	% Clustering
	[~, shapemes] = kmeans(SCSampled, dCodebook, 'EmptyAction', 'singleton');

	knnObj = ExhaustiveSearcher(shapemes);
	bow = zeros(nImages, dCodebook);
	for i = 1:nImages
		nearestInd = knnsearch(knnObj, shapeContexts{i});
		bow(i, :) = histc(nearestInd', 1:dCodebook);
	end

	% Normalization
	bow = bow / nPoints;
end