Init calculateNcut, Init demos

Assignment_2/calculateNcut.m

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+function nCutValue = calculateNcut (anAffinityMat , clusterIdx)
+%Implementation the NCut metric calculation
+%   Usage nCutValue = calculateNcut (anAffinityMat , clusterIdx), where:
+%       Inputs
+%       - anAffinityMat is a rectagular, symmetrical affinity matrix
+%           representation of an image
+%       - clusterIdx is a vector storing the cluster Id of each node
+%       Output
+%       - nCutValue is a vector storing the cluster Id of each node
+
+    % Gets the unique cluster IDs
+    clusterIds = unique(clusterIdx);
+    if size(clusterIds, 1) ~= 2
+        error('Too many different clusters! Number of clusters should be two');
+    end
+
+    clusterOneIndices = (clusterIdx == clusterIds(1));
+    clusterTwoIndices = (clusterIdx == clusterIds(2));
+
+    nCutValue = 2 - ...
+        (sum(sum(anAffinityMat(clusterOneIndices, clusterOneIndices'))) / ...
+        sum(sum(anAffinityMat(clusterOneIndices, :))) + ...
+        sum(sum(anAffinityMat(clusterTwoIndices, clusterTwoIndices'))) / ...
+        sum(sum(anAffinityMat(clusterTwoIndices, :))));
+end

Assignment_2/demo1.m

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+clear
+clear all
+
+% For reproducibility
+rng(1);
+
+load('dip_hw_2.mat');
+
+clusters = mySpectralClustering(d1a, 2);
+clusters = clusters ./ 2;
+%clusters = reshape(clusters, 4, []);
+figure();
+imshow(clusters);
+
+clusters = mySpectralClustering(d1a, 3);
+clusters = clusters ./ 3;
+%clusters = reshape(clusters, 4, []);
+figure();
+imshow(clusters);
+
+clusters = mySpectralClustering(d1a, 4);
+clusters = clusters ./ 4;
+%clusters = reshape(clusters, 4, []);
+figure();
+imshow(clusters);

Assignment_2/demo2.m

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+clear
+clear all
+
+% For reproducibility
+rng(1);
+
+load('dip_hw_2.mat');
+
+%% Produces the affinity graphs for both images
+graph1 = Image2Graph(d2a);
+graph2 = Image2Graph(d2b);
+
+%% Executes experiments for the first image
+figure();
+imshow(d2a);
+
+clusters = mySpectralClustering(graph1, 2);
+clusters = clusters ./ 2;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+clusters = mySpectralClustering(graph1, 3);
+clusters = clusters ./ 3;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+clusters = mySpectralClustering(graph1, 4);
+clusters = clusters ./ 4;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+%% Executes experiments for the second image
+figure();
+imshow(d2b);
+
+clusters = mySpectralClustering(graph2, 2);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 2;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);
+
+clusters = mySpectralClustering(graph2, 3);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 3;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);
+
+clusters = mySpectralClustering(graph2, 4);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 4;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);

Assignment_2/demo3a.m

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+clear
+clear all
+
+% For reproducibility
+rng(1);
+
+load('dip_hw_2.mat');
+
+%% Produces the affinity graphs for both images
+graph1 = Image2Graph(d2a);
+graph2 = Image2Graph(d2b);
+
+%% Executes non recursive experiments for the first image
+figure();
+imshow(d2a);
+
+clusters = myNCuts(graph1, 2);
+clusters = clusters ./ 2;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+clusters = myNCuts(graph1, 3);
+clusters = clusters ./ 3;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+clusters = myNCuts(graph1, 4);
+clusters = clusters ./ 4;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+%% Executes non recursive experiments for the second image
+figure();
+imshow(d2b);
+
+clusters = myNCuts(graph2, 2);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 2;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);
+
+clusters = myNCuts(graph2, 3);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 3;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);
+
+clusters = myNCuts(graph2, 4);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 4;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);

Assignment_2/demo3b.m

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+clear
+clear all
+
+% For reproducibility
+rng(1);
+
+load('dip_hw_2.mat');
+
+%% Produces the affinity graphs for both images
+graph1 = Image2Graph(d2a);
+graph2 = Image2Graph(d2b);
+
+%% Executes recursive experiments for the first image
+figure();
+imshow(d2a);
+
+clusters = myNCuts(graph1, 2);
+NCut1 = calculateNcut(graph1, clusters);
+
+clusters = clusters - 1;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+%% Executes recursive experiments for the second image
+figure();
+imshow(d2b);
+
+clusters = myNCuts(graph2, 2);
+NCut2 = calculateNcut(graph2, clusters);
+
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+
+clusters = clusters - 1;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);

Assignment_2/demo3c.m

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+clear
+clear all
+
+% For reproducibility
+rng(1);
+
+load('dip_hw_2.mat');
+
+%% Produces the affinity graphs for both images
+graph1 = Image2Graph(d2a);
+graph2 = Image2Graph(d2b);
+
+%% Executes recursive experiments for the first image
+figure();
+imshow(d2a);
+
+clusters = recursiveNCuts(graph1);
+clusters = clusters ./ 2;
+clusters = reshape(clusters, size(d2a, 1), []);
+figure();
+imshow(clusters);
+
+%% Executes recursive experiments for the second image
+figure();
+imshow(d2b);
+
+clusters = recursiveNCuts(graph2);
+figure();
+imshow(meanClustersColorRGB(d2b, reshape(clusters, size(d2b, 1), [])));
+% clusters = clusters ./ 2;
+% clusters = reshape(clusters, size(d2a, 1), []);
+% figure();
+% imshow(clusters);

Assignment_2/meanClustersColorRGB.m

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+function segIm = meanClustersColorRGB(image, clusters)
+%MEANCLUSTERSCOLOR Summary of this function goes here
+%   Detailed explanation goes here
+
+    redChannel = image(:, :, 1);
+    greenChannel = image(:, :, 2);
+    blueChannel = image(:, :, 3);
+
+    segImR = clusters;
+    segImG = clusters;
+    segImB = clusters;
+
+    for cluster = 1:max(max(clusters))
+        meanR = mean(redChannel(clusters == cluster));
+        meanG = mean(greenChannel(clusters == cluster));
+        meanB = mean(blueChannel(clusters == cluster));
+
+        segImR(clusters == cluster) = meanR;
+        segImG(clusters == cluster) = meanG;
+        segImB(clusters == cluster) = meanB;
+    end
+
+    segIm = zeros(size(image, 1), size(image, 2), 3);
+    segIm(:, :, 1) = segImR;
+    segIm(:, :, 2) = segImG;
+    segIm(:, :, 3) = segImB;
+end
+

Assignment_2/recursiveNCuts.m

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+function clusters = recursiveNCuts(graph)
+%RECURSIVENCUTS Summary of this function goes here
+%   Detailed explanation goes here
+
+    clusters = myNCuts(graph, 2);
+    
+    if (nnz(clusters == 1) < 5 || nnz(clusters == 2) < 5)
+        return;
+    end
+    
+    NCut = calculateNcut(graph, clusters);
+    if (NCut > 0.85)
+        return;
+    end
+    
+    clusterOneIndices = (clusters == 1);
+    clusterTwoIndices = (clusters == 2);
+    
+    firstSubClusters = recursiveNCuts(graph(clusterOneIndices, clusterOneIndices'));
+    secondSubClusters = recursiveNCuts(graph(clusterTwoIndices, clusterTwoIndices'));
+    
+    secondSubClusters = secondSubClusters + size(unique(firstSubClusters), 1);
+    
+    clusters(clusterOneIndices) = firstSubClusters;
+    clusters(clusterTwoIndices) = secondSubClusters;
+end