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Author: SeyedMuhammadHosseinMousavi

GasNN.m

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+function NGNnetwork = GasNN(X, params)
+
+%% Load
+nData = size(X,1);
+nDim = size(X,2);
+X = X(randperm(nData),:);
+Xmin = min(X);
+Xmax = max(X);
+
+%% Params
+N = params.N;
+MaxIt = params.MaxIt;
+tmax = params.tmax;
+epsilon_initial = params.epsilon_initial;
+epsilon_final = params.epsilon_final;
+lambda_initial = params.lambda_initial;
+lambda_final = params.lambda_final;
+T_initial = params.T_initial;
+T_final = params.T_final;
+
+%% Initialization
+w = zeros(N, nDim);
+for i = 1:N
+w(i,:) = unifrnd(Xmin, Xmax);
+end
+C = zeros(N, N);
+t = zeros(N, N);
+tt = 0;
+
+%% Body
+for it = 1:MaxIt
+for l = 1:nData
+% Slect Input Vector
+x = X(l,:);
+% Competion and Ranking
+d = pdist2(x,w);
+[~, SortOrder] = sort(d);
+% Calculate Parameters
+epsilon = epsilon_initial*(epsilon_final/epsilon_initial)^(tt/tmax);
+lambda = lambda_initial*(lambda_final/lambda_initial)^(tt/tmax);
+T = T_initial*(T_final/T_initial)^(tt/tmax);
+% Adaptation
+for ki = 0:N-1
+i=SortOrder(ki+1);
+w(i,:) = w(i,:) + epsilon*exp(-ki/lambda)*(x-w(i,:));
+end
+tt = tt + 1;
+% Creating Links
+i = SortOrder(1);
+j = SortOrder(2);
+C(i,j) = 1;
+C(j,i) = 1;
+t(i,j) = 0;
+t(j,i) = 0;
+% Aging
+t(i,:) = t(i,:) + 1;
+t(:,i) = t(:,i) + 1;
+% Remove Old Links
+L = t(i,:)>T;
+C(i, L) = 0;
+C(L, i) = 0;
+end
+disp(['Iteration ' num2str(it)]);
+end
+%% Results
+NGNnetwork.w = w;
+NGNnetwork.C = C;
+NGNnetwork.t = t;
+end

NGNImageSegmentation.m

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+% Image Segmentation and Quantization by Neural Gas Network (NGN)
+% Define number of segments and iterations and get the output. 
+% Org is image. You can use your image. 
+% ParVal.N is Number of Segments
+% ParVal.MaxIt is Number of runs
+%----------------------------------------------------------------------
+clc;
+clear;
+close all;
+
+%% Load Image
+Org=imread('Veg.jpg');
+X = rgb2gray(Org);
+X=double(X);
+img=X;
+X=X(:)';
+
+%% Neural Gas Network (NGN) Parameters
+
+ParVal.N = 16; % Number of Segments
+ParVal.MaxIt = 50; % Number of runs
+
+ParVal.tmax = 100000;
+
+ParVal.epsilon_initial = 0.3;
+ParVal.epsilon_final = 0.02;
+ParVal.lambda_initial = 2;
+ParVal.lambda_final = 0.1;
+ParVal.T_initial = 5;
+ParVal.T_final = 10;
+
+%% Training Neural Gas Network
+NGNnetwok = GasNN(X, ParVal);
+
+%% Vector to image and plot
+Weight=sum(round(rescale(NGNnetwok.w,1,ParVal.N)));
+Weight=round(rescale(Weight,1,ParVal.N));
+indexed=reshape(Weight(1,:),size(img));
+segmented = label2rgb(indexed); 
+% Plot Res
+figure('units','normalized','outerposition',[0 0 1 1])
+subplot(2,2,1)
+imshow(Org,[]); title('Original');
+subplot(2,2,2)
+imshow(img,[]); title('Grey');
+subplot(2,2,3)
+imshow(segmented);
+title(['Segmented in [' num2str(ParVal.N) '] Segments']);
+subplot(2,2,4)
+imshow(indexed,[]);
+title(['Quantized in [' num2str(ParVal.N) '] Thresholds']);
+
+