example_classifier.m

function example_classifier

% change this path if you install the VOC code elsewhere
addpath([cd '/VOCcode']);

% initialize VOC options
VOCinit;

% train and test classifier for each class
for i=1:VOCopts.nclasses
    cls=VOCopts.classes{i};
    classifier=train(VOCopts,cls);                           % train classifier
    test(VOCopts,cls,classifier);                            % test classifier
    [recall,prec,ap]=VOCevalcls(VOCopts,'comp1',cls,true);   % compute and display PR
    
    if i<VOCopts.nclasses
        fprintf('press any key to continue with next class...\n');
        drawnow;
        pause;
    end
end

% train classifier
function classifier = train(VOCopts,cls)

% load training set for class
[ids,classifier.gt]=textread(sprintf(VOCopts.clsimgsetpath,cls,VOCopts.trainset),'%s %d');

% extract features for each image
classifier.FD=zeros(0,length(ids));
tic;
for i=1:length(ids)
    % display progress
    if toc>1
        fprintf('%s: train: %d/%d\n',cls,i,length(ids));
        drawnow;
        tic;
    end

    fdp=sprintf(VOCopts.exfdpath,ids{i});
	if exist(fdp,'file')
        % load features
        load(fdp,'fd');
    else
        % compute and save features
        I=imread(sprintf(VOCopts.imgpath,ids{i}));
        fd=extractfd(VOCopts,I);
        save(fdp,'fd');
    end
    
    classifier.FD(1:length(fd),i)=fd;
end

% run classifier on test images
function test(VOCopts,cls,classifier)

% load test set ('val' for development kit)
[ids,gt]=textread(sprintf(VOCopts.imgsetpath,VOCopts.testset),'%s %d');

% create results file
fid=fopen(sprintf(VOCopts.clsrespath,'comp1',cls),'w');

% classify each image
tic;
for i=1:length(ids)
    % display progress
    if toc>1
        fprintf('%s: test: %d/%d\n',cls,i,length(ids));
        drawnow;
        tic;
    end
    
    fdp=sprintf(VOCopts.exfdpath,ids{i});
    if exist(fdp,'file')
        % load features
        load(fdp,'fd');
    else
        % compute and save features
        I=imread(sprintf(VOCopts.imgpath,ids{i}));
        fd=extractfd(VOCopts,I);
        save(fdp,'fd');
    end

    % compute confidence of positive classification
    c=classify(VOCopts,classifier,fd);
    
    % write to results file
    fprintf(fid,'%s %f\n',ids{i},c);
end

% close results file
fclose(fid);

% trivial feature extractor: compute mean RGB
function fd = extractfd(VOCopts,I)

fd=squeeze(sum(sum(double(I)))/(size(I,1)*size(I,2)));

% trivial classifier: compute ratio of L2 distance betweeen
% nearest positive (class) feature vector and nearest negative (non-class)
% feature vector
function c = classify(VOCopts,classifier,fd)

d=sum(fd.*fd)+sum(classifier.FD.*classifier.FD)-2*fd'*classifier.FD;
dp=min(d(classifier.gt>0));
dn=min(d(classifier.gt<0));
c=dn/(dp+eps);