海明嵌入Hamming Embedding生成Binary Signature之matlab程序代码

% This code generates 128-bit hamming code "bs" for SIFT features %128-D % "bs": binary signature
%
% Bug fixed by Liang Zheng on 01/26/2015.

%% load previous data
feat_file = '100000.siftgeo';  % folder of holidays features %holidays特征
word_file = '100000_word.mat'; % holidays visual word %visual_word:3*1125 double
P = importdata('projection_matrix_128bits.mat');% load projection matrix %映射矩阵 %P：128*128 double %mat文件怎么计算来的？？
thres = importdata('thres_20k_128bits.mat');% load median matrix中位数矩阵 %thres:128*20000 double

% a matrix for converting 01 strings into 0-255 uint8 data %转换01串-> 0~255 % 将二进制转为uint8的十进制
% uint8 data
nbits = 128; %nbits:number of bits for SIFT feature
m = 2.^(0:7); %m = [1 2 4 8 ... 128] % (0:7)是个向量 [0, 1, 2, 3, 4, 5, 6, 7]
M = m; % [1 2 4 8 ... 128]
for i = 2:nbits/8 %2~128/8 % 2~16 % 循环15次，最后得到的矩阵大小是 <16,(8*16)>即<16, 128>
% 斜对角串联，每次增加一行，增加size(m, 2)=8列，每次斜对角方向增加一个m，其他部位填充0
M = blkdiag (M, m); %Block diagonal concatenation of matrix input arguments
end
bin2compactbin = M;

% for each image, calculate the HE signatures of its features 计算特征的HE
% signature
[data, meta] = siftgeo_read(feat_file); % load features %feat_file = '100000.siftgeo';  data: n*128
data = data'; %转置后，data:128*n

% rootSIFT
sum_val = sum(data); %sum_val: 1*128 %1、求和
for i = 1:128
data(i, :) = data(i, :)./sum_val; %2、 ./ 点除
end
data = single(sqrt(data));  %3、根方，并转为single类型

visual_word = importdata(word_file); % load visual word %word_file = '100000_word.mat'; %visual_word:3*1125 %对应descriptors的visual words
nsift = size(visual_word, 2);  %nsift = 1125, 1125个sift keypoints，size(visual_word, 1)是MA（kNN），size(visual_word, 2)是sift descriptors的个数

%% feature projection and binarization %特征映射与二值化
temp_data = P*data; % projected feature %data是RootSIFT特征 %% P = importdata('projection_matrix_128bits.mat');
kNN = 3; % number of visual words assigned to each SIFT feature %visual word就是index，赋给每个SIFT feature的visual words
hamming_data = zeros(128, nsift*kNN);% note, multiple assignment is implied here. kNN = 3. %注意 MA = kNN = 3,即每个feature对应3个visual words, hamming_data:128*(nsift*kNN)
if ~isempty(visual_word) %非空
for i = 1:kNN %1~3
vw_index = visual_word(i, :); %visual_word:3*1125, 取每一行i，vw_index: 1*1125

thresh = thres(:,vw_index); %thres = importdata('thres_20k_128bits.mat'); thres：128*20000，截取thres矩阵，新得到的thres:128*1125
IDX = i:3:((nsift-1)*3+i);  %nsift是rootSFIT个数，1125， i:3：(1124*3+i)
hamming_data(:, IDX) = temp_data - thresh; % projected data minus median matrix
end
hamming_data(hamming_data > 0) = 1; %hamming_data > 0的部分置为1，其余部分置为0
hamming_data(hamming_data <=0 ) = 0;
% bs: binary signature
bs = uint8 (bin2compactbin*hamming_data); % convert 01 strings into uint8 data  %bin2compactbin = M;
else %visual_word为空
bs = [];
end

% save file. bs is used as the binary signature of SIFT features
write_file = '100000_he.txt';
fid = fopen(write_file, 'w'); %创建100000_he.txt文件
fwrite(fid, bs, 'uint8'); %将binary signature写入100000_he.txt
fclose(fid);