利用adaboost方法进行目标分类识别的例程

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Simple demo of Gentle Boost with stumps and 2D data % % % Implementation of gentleBoost. The algorithm is described in: % Friedman, J. H., Hastie, T. and Tibshirani, R. % "Additive Logistic Regression: a Statistical View of Boosting." (Aug. 1998) % atb, 2003 % torralba@ai.mit.edu clear all % Define plot style parameters plotstyle.colors = {'gs', 'ro'}; % color for each class plotstyle.range = [-50 50 -50 50]; % data range %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Create data: use the mouse to build the training dataset. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure(1); clf axis(plotstyle.range); hold on axis('square') title('Left button = class +1, right button = class -1. Press any key to finish.') i = 0; clear X Y while 1 [x,y,c] = ginput(1); if ismember(c, [1 3]) i = i + 1; X(1,i) = x; X(2,i) = y; Y(i) = (c==1) - (c==3); % class = {-1, +1} plot(x, y, plotstyle.colors{(Y(i)+3)/2}, 'MarkerFaceColor', plotstyle.colors{(Y(i)+3)/2}(1), 'MarkerSize', 10); else break end end [Nfeatures, Nsamples] = size(X); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Boosting parameters: Nrounds = 100; % Create test data: it is created on a grid [x1, x2] = meshgrid(linspace(plotstyle.range(1), plotstyle.range(2), 100)); [nrows,ncols] = size(x1); xt = [x1(:) x2(:)]'; % Initialize output of strong classifiers on training and test set Fx = 0; FxT = 0; % Initialize to one all the weights for training samples w = ones(1, Nsamples); figure % Run boosting iterations and plot intermediate results for m = 1:Nrounds disp(sprintf('Round %d', m)) % Weak regression stump: It is defined by four parameters (a,b,k,th) % f_m = a * (x_k > th) + b [k, th, a , b] = selectBestRegressionStump(X, Y, w); % Updating and computing classifier output on training set fm = (a * (X(k,:)>th) + b); Fx = Fx + fm; % Updating and computing classifier output on test set fmT = (a * (xt(k,:)>th) + b); FxT = FxT + fmT; % Visualization subplot(121) imagesc([plotstyle.range(1:2)], [plotstyle.range(3:4)], reshape(fmT, [nrows,ncols])) colormap(gray(256)) hold on plotw(X(1,:), X(2,:), w, Y, plotstyle); axis('off') subplot(122) imagesc([plotstyle.range(1:2)], [plotstyle.range(3:4)], reshape(FxT>0, [nrows,ncols])) colormap(gray(256)) hold on plotw(X(1,:), X(2,:), ones(size(w)), Y, plotstyle); title('Strong classifier') axis('off') % Reweight training samples w = w .* exp(-Y.*fm); disp('Press a key to see next round') pause end