L1.m

function L1(numOfReturnedImages, queryImageFeatureVector, dataset, folder_name, img_ext)
% input:
%   numOfReturnedImages : num of images returned by query
%   queryImageFeatureVector: query image in the form of a feature vector
%   dataset: the whole dataset of images transformed in a matrix of
%   features
% 
% output: 
%   plot: plot images returned by query

% extract image fname from queryImage and dataset
query_image_name = queryImageFeatureVector(:, end);
dataset_image_names = dataset(:, end);

queryImageFeatureVector(:, end) = [];
dataset(:, end) = [];

% compute manhattan distance
manhattan = zeros(size(dataset, 1), 1);
for k = 1:size(dataset, 1)
%     manhattan(k) = sum( abs(dataset(k, :) - queryImageFeatureVector) );
    % ralative manhattan distance
    manhattan(k) = sum( abs(dataset(k, :) - queryImageFeatureVector) ./ ( 1 + dataset(k, :) + queryImageFeatureVector ) );
end

% add image fnames to manhattan
manhattan = [manhattan dataset_image_names];

% sort them according to smallest distance
[sortedDist indx] = sortrows(manhattan);
sortedImgs = sortedDist(:, 2);

% clear axes
arrayfun(@cla, findall(0, 'type', 'axes'));

% display query image
str_name = int2str(query_image_name);
queryImage = imread( strcat(folder_name, '\', str_name, img_ext) );
subplot(3, 7, 1);
imshow(queryImage, []);
title('Query Image', 'Color', [1 0 0]);

% dispaly images returned by query
for m = 1:numOfReturnedImages
    img_name = sortedImgs(m);
    img_name = int2str(img_name);
    str_name = strcat(folder_name, '\', img_name, img_ext);
    returnedImage = imread(str_name);
    subplot(3, 7, m+1);
    imshow(returnedImage, []);
end

end