selective_search.m

function all_boxes = selective_search(image_filenames, output_filename)

addpath('Dependencies');

if(~exist('anigauss'))
    mex Dependencies/anigaussm/anigauss_mex.c Dependencies/anigaussm/anigauss.c -output anigauss
end

if(~exist('mexCountWordsIndex'))
    mex Dependencies/mexCountWordsIndex.cpp
end

if(~exist('mexFelzenSegmentIndex'))
    mex Dependencies/FelzenSegment/mexFelzenSegmentIndex.cpp -output mexFelzenSegmentIndex;
end

colorTypes = {'Hsv', 'Lab', 'RGI', 'H', 'Intensity'};
colorType = colorTypes{1}; % Single color space for demo

% Here you specify which similarity functions to use in merging
simFunctionHandles = {@SSSimColourTextureSizeFillOrig, @SSSimTextureSizeFill, @SSSimBoxFillOrig, @SSSimSize};
simFunctionHandles = simFunctionHandles(1:2); % Two different merging strategies

% Thresholds for the Felzenszwalb and Huttenlocher segmentation algorithm.
% Note that by default, we set minSize = k, and sigma = 0.8.
k = 200; % controls size of segments of initial segmentation.
minSize = k;
sigma = 0.8;

% Process all images.
all_boxes = {};
for i=1:length(image_filenames)
    im = imread(image_filenames{i});
    [boxes blobIndIm blobBoxes hierarchy] = Image2HierarchicalGrouping(im, sigma, k, minSize, colorType, simFunctionHandles);
    all_boxes{i} = BoxRemoveDuplicates(boxes);
end

if nargin > 1
    all_boxes
    save(output_filename, 'all_boxes', '-v7');
end