diff classify_scenes.m @ 1:96b1b8697b60

challenge version

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/classify_scenes.m	Mon Nov 04 10:43:51 2013 +0000
@@ -0,0 +1,107 @@
+% Copyright 2013 MUSIC TECHNOLOGY GROUP, UNIVERSITAT POMPEU FABRA
+%
+% Written by Gerard Roma <[e-mail]>
+%
+% This program is free software: you can redistribute it and/or modify
+% it under the terms of the GNU Affero General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU Affero General Public License for more details.
+%
+% You should have received a copy of the GNU Affero General Public License
+% along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+function [ predict_label ] = classify_scenes(tmp_path, train_file,test_file, output_file, grid_search)
+    addpath('rastamat/');
+    addpath('libsvm/')
+    
+    classes = {'bus' 'busystreet' 'office' 'openairmarket' 'park' 'quietstreet' 'restaurant' 'supermarket' 'tube' 'tubestation'};
+    
+    [tr_names,tr_labels] = loadClassificationOutput(train_file);
+    [te_names,te_labels] = loadClassificationOutput(test_file);
+    
+    
+    %% extract features
+    disp('analyzing training files');
+    train_features = analyze_files(tr_names,tmp_path);
+    disp('analyzing test files');
+    test_features = analyze_files(te_names, tmp_path);
+    train_labels = [];
+    test_labels = ones(length(te_names),1);
+    
+    train_labels = get_class_indices(tr_labels);
+    
+    [train_z, mu, sigma] = zscore(train_features);
+    MU = repmat(mu,size(test_features,1),1);
+    SIGMA = repmat(sigma,size(test_features,1),1);
+    test_z = (test_features -MU)./SIGMA;
+    
+    
+    %% SVM grid Search inspired in http://labrosa.ee.columbia.edu/projects/consumervideo/
+    
+    if grid_search
+        disp('performing grid search');
+        tuning_data_fraction = 0.9;
+        train_size = round(tuning_data_fraction*size(train_z,1));
+        p = randperm(size(train_z,1));
+        trainX = train_z(p(1:train_size),:);
+        validateX = train_z(p(train_size+1:end),:);
+        trainY = get_class_indices(tr_labels(p(1:train_size)));
+        validateY = get_class_indices(tr_labels(p(train_size+1:end)));
+                
+        
+        gamma = 2.^[-10:1:10];
+        C = 2.^[0:13];
+        
+        params = zeros(length(gamma),length(C));
+        best_a = 0;
+        best_g = 0;
+        best_C = 0;
+     
+        for gi= 1:length(gamma)
+            for Ci = 1:length(C)
+                m = svmtrain(trainY', trainX, sprintf('-c %d -g %2.5f -q',C(Ci),gamma(gi)));
+                [p,a] = svmpredict(validateY', validateX, m, '-q');
+                if a(1) > best_a
+                    best_C = C(Ci);
+                    best_g = gamma(gi);
+                    best_a = a(1);
+                end
+            end
+        end
+        disp('grid search done');
+    else
+        best_C=70;
+        best_g = 0.003;
+    end
+ 
+    
+    model = svmtrain(train_labels', train_z, sprintf('-c %d -g %2.5f -q',best_C,best_g));
+    [predict_indices, accuracy_obj, prob_values] = svmpredict(test_labels, test_z, model,'-q');
+    predict_label = classes(predict_indices);
+    
+    
+    outfd = fopen(output_file,'w+');
+    
+    for i = 1:length(te_names)
+         fprintf(outfd,'%s\t',[char(te_names(i))]);
+         fprintf(outfd,'%s\n',[char(predict_label(i))]);
+    end
+    fclose(outfd);    
+    
+    function idx = get_class_indices(labels)
+        idx = [];            
+        for i=1:length(labels)
+        class =  find(strcmp(classes,labels(i)));
+        idx(i) = class;
+    end
+        
+end
+
+    
+end
+