diff --git a/Assignment_2/Image2Graph.m b/Assignment_2/Image2Graph.m
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+++ b/Assignment_2/Image2Graph.m
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+function myAffinityMat = Image2Graph (imIn)
+%Image2Graph produces a graph, in the form of a matrix, from an image
+%   Usage myAffinityMat = Image2Graph (imIn), where:
+%       Inputs
+%       - imIn is the input image
+%       Output
+%       - myAffinityMat is the graph produced
+
+    % Initializes helper variables
+    imageWidth = size(imIn, 2);
+    imageHeight = size(imIn, 1);
+
+    % Initializes output matrix
+%     myAffinityMat = zeros(imageWidth * imageHeight, imageWidth * ...
+%         imageHeight);
+    
+%     for row = 1:imageHeight
+%         for column = 1:imageWidth
+%             % Calculates distance matrix for the current element
+%             diff = imIn - imIn(row, column, :);
+%             dist = sum(diff .^ 2, 3) .^ 0.5;
+%             
+%             % Calculates affinity matrix
+%             myAffinityMat((row - 1) * imageWidth + column, :) = ...
+%                 reshape((1 ./ exp(dist)).', 1, []);
+%         end
+%     end
+    
+    som = reshape(imIn, 1, [], 3);
+    diff = repmat(som, imageWidth * imageHeight, 1, 1) - permute(som, [2 1 3]);
+    dist = sum(diff .^ 2, 3) .^ 0.5;
+    myAffinityMat = 1 ./ exp(dist);
+end
diff --git a/Assignment_2/Image2Graph.m~ b/Assignment_2/Image2Graph.m~
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+function myAffinityMat = Image2Graph (imIn)
+%Image2Graph produces a graph, in the form of a matrix, from an image
+%   Usage myAffinityMat = Image2Graph (imIn), where:
+%       Inputs
+%       - imIn is the input image
+%       Output
+%       - myAffinityMat is the graph produced
+
+    imageWidth = size(imIn, 2);
+    imageHeight = size(imIn, 1);
+    imageChannels = size(imIn, 3);
+
+    %myAffinityMat = 1 / exp(norm(imIn(:, :)));
+    myAffinityMat = zeros(imageWidth * imageHeight, imageWidth * imageHeight);
+    
+    for row = 1:imageHeight
+        for column = 1:imageWidth
+            diff = imIn - imIn(row, column, :);
+            %diffT = permute(diff, [2 1 3]);
+            
+            dist = sum(diff .^ 2, 3) .^ 0.5;
+            
+%             dist = zeros(imageWidth, imageHeight);
+%             for channel = 1:imageChannels
+%                 dist = dist + sqrt(sum (diff(:, :, channel) * diffT(:, :, channel)));
+%                 dist = dist + sqrt(diff(:, :, channel) * diffT(:, :, channel));
+%             end
+            
+            som = 1 ./ exp(dist);
+            som2 = reshape(som, 1, []);
+            som2 = reshape(som.', 1, []);
+            
+            myAffinityMat((row - 1) * imageWidth + column, :) = reshape((1 ./ exp(dist), 1, []);
+        end
+    end
+end
diff --git a/Assignment_2/README.md b/Assignment_2/README.md
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+++ b/Assignment_2/README.md
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+# Second deliverable
+
+In the second deliverable we were tasked to implement a number of functions that:
+- produce a graph representation of an image
+- do image segmentation using spectral clustering
+- do image segmentation using the normalized cuts (N-cuts) method, in both an imperative and recursive way
+- execute a series of experiments to test the results of our code
\ No newline at end of file
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