clear;?
close?all;
clc;
%?加載用于訓(xùn)練的sift描述子向量
load?siftTrain.mat;

%?將用于訓(xùn)練的sift描述子向量均分為16段，每段8維
sub_vector?=?cell（161）;
for?i?=?1?:?16
????sub_vector{i}?=?siftDescriptorTrain（（i-1）*8+1:8*i:）;
end

C?=?cell（161）;
Idx?=?cell（161）;
opts?=?statset（‘MaxIter‘300）;
%?對(duì)每一段進(jìn)行k-means聚類，k=64
for?i?=?1?:?16
????[Idx{i}??C{i}]?=?kmeans（sub_vector{i}‘64‘Options‘opts）;
end

dist?=?cell（161）;
for?i?=?1?:?16
????dist{i}?=?zeros（6464）;
end

%?生成16個(gè)distance?matrix
for?i?=?1?:?16
????for?j?=?1?:?64
????????tmpFeat?=?repmat（C{i}（j:）?64?1）;
????????dist{i}（j:）?=?（sum（（tmpFeat?-?C{i}）.^22））‘;
????end
end

%tic

%%?Load?two?images?and?their?SIFT?features
src_1?=?‘./test?images/ukbench00000.jpg‘;
src_2?=?‘./test?images/ukbench00003.jpg‘;
ext1?=?‘.dsift‘;?%‘.vsift_640‘;?%?extension?name?of?SIFT?file
ext2?=?‘.dsift‘;?%‘.vsift_640‘;?%?extension?name?of?SIFT?file
siftDim?=?128;

%%?load?image?
im_1?=?imread（src_1）;
im_2?=?imread（src_2）;

%%?讀取sift特征
featPath_1?=?[src_1?ext1];
featPath_2?=?[src_2?ext2];

fid_1?=?fopen（featPath_1?‘rb‘）;
featNum_1?=?fread（fid_1?1?‘int32‘）;?%??????SIFT?????????
SiftFeat_1?=?zeros（siftDim?featNum_1）;?
paraFeat_1?=?zeros（4?featNum_1）;
for?i?=?1?:?featNum_1?%???????SIFT????
????SiftFeat_1（:?i）?=?fread（fid_1?siftDim?‘uchar‘）;?%?????128???????
????paraFeat_1（:?i）?=?fread（fid_1?4?‘float32‘）;?????%?????[x?y?scale?orientation]???
end
fclose（fid_1）;

fid_2?=?fopen（featPath_2?‘rb‘）;
featNum_2?=?fread（fid_2?1?‘int32‘）;?%??????SIFT?????????
SiftFeat_2?=?zeros（siftDim?featNum_2）;
paraFeat_2?=?zeros（4?featNum_2）;
for?i?=?1?:?featNum_2?%???????SIFT????
????SiftFeat_2（:?i）?=?fread（fid_2?siftDim?‘uchar‘）;?%?????128???????
????paraFeat_2（:?i）?=?fread（fid_2?4?‘float32‘）;?????%?????[x?y?scale?orientation]???
end
fclose（fid_2）;

%%?將第一張待匹配圖像的sift特征向量分為16段
feat1_vector?=?cell（161）;
for?i?=?1?:?16
????feat1_vector{i}?=?SiftFeat_1（（i-1）*8+1:8*i:）;
end

%%?判斷待匹配圖像的每個(gè)特征向量的每一段各屬于哪一類
minposition?=?zeros（16?featNum_1）;?
for?i?=?1?:?16
????for?j?=1?:?featNum_1
????????tmpFeat?=?repmat（feat1_vector{i}（:j）164）;
????????d1?=?sum（（tmpFeat?-?C{i}‘）.^2）;?%?distance
????????minposition（ij）?=?find（d1==min（d1））;
????end
end

%%?將第二張待匹配圖像的sift特征向量分為16段
feat2_vector?=?cell（161）;
for?i?=?1?:?16
????feat2_vector{i}?=?SiftFeat_2（（i-1）*8+1:8*i:）;
end

%%?判斷待匹配圖像的每個(gè)特征向量的每一段各屬于哪一類
minposition2?=?zeros（16?featNum_2）;?
for?i?=?1?:?16
????for?j?=1?:?featNum_2
????????tmpFeat?=?repmat（feat2_vector{i}（:j）164）;
????????d1?=?sum（（tmpFeat?-?C{i}‘）.^2）;?%?distance
????????minposition2（ij）?=?find（d1==min（d1））;
????end
end

%%?在feat2中找離feat1的距離滿足一定條件的特征，構(gòu)成匹配
matchInd?=?zeros（featNum_1?1）;
matchDis?=?zeros（featNum_1?1）;
for?i?=?1?:?featNum_1
????d?=?zeros（1featNum_2）;
????for?k?=?1?:?16
????????d（1?:?featNum_2）?=?d（1?:?featNum_2）?+dist{k}（?minposition（ki）??minposition2（k1?:?featNum_2）?）;
????end
????matchDis（i）?=?min（d）;
????[v?ind]?=?sort（d）;
????if?v（1）?/?v（2）?<=?0.64
????????matchInd（i）?=?ind（1）;
????end
end


%%?可視化
[row?col?cn]?=?siz

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-11-15?15:42??test?images\
?????文件??????243673??2013-12-05?23:17??test?images\ukbench00000.jpg
?????文件??????696244??2014-03-16?05:45??test?images\ukbench00000.jpg.dsift
?????文件??????246526??2013-12-05?23:17??test?images\ukbench00001.jpg
?????文件??????545332??2014-03-16?05:45??test?images\ukbench00001.jpg.dsift
?????文件??????218653??2013-12-05?23:17??test?images\ukbench00002.jpg
?????文件??????741028??2014-03-16?05:45??test?images\ukbench00002.jpg.dsift
?????文件??????269422??2013-12-05?23:17??test?images\ukbench00003.jpg
?????文件??????674068??2014-03-16?05:46??test?images\ukbench00003.jpg.dsift
?????文件??????152703??2013-12-05?23:17??test?images\ukbench00012.jpg
?????文件??????154228??2014-03-16?05:46??test?images\ukbench00012.jpg.dsift
?????文件??????155830??2013-12-05?23:17??test?images\ukbench00013.jpg
?????文件??????154948??2014-03-16?05:46??test?images\ukbench00013.jpg.dsift
?????文件??????175983??2013-12-05?23:17??test?images\ukbench00014.jpg
?????文件??????103828??2014-03-16?05:46??test?images\ukbench00014.jpg.dsift
?????文件??????168753??2013-12-05?23:17??test?images\ukbench00015.jpg
?????文件??????107284??2014-03-16?05:46??test?images\ukbench00015.jpg.dsift
?????文件??????153642??2006-10-05?01:11??test?images\ukbench00052.jpg
?????文件??????140404??2014-03-16?05:47??test?images\ukbench00052.jpg.dsift
?????文件??????160703??2006-10-05?01:11??test?images\ukbench00053.jpg
?????文件??????149332??2014-03-16?05:47??test?images\ukbench00053.jpg.dsift
?????文件??????144807??2006-10-05?01:11??test?images\ukbench00054.jpg
?????文件??????153508??2014-03-16?05:47??test?images\ukbench00054.jpg.dsift
?????文件??????151801??2006-10-05?01:11??test?images\ukbench00055.jpg
?????文件??????139252??2014-03-16?05:47??test?images\ukbench00055.jpg.dsift
?????文件??????172114??2006-10-05?01:11??test?images\ukbench00080.jpg
?????文件??????114628??2014-03-16?05:48??test?images\ukbench00080.jpg.dsift
?????文件??????181878??2006-10-05?01:11??test?images\ukbench00081.jpg
?????文件??????135940??2014-03-16?05:48??test?images\ukbench00081.jpg.dsift
?????文件??????154145??2006-10-05?01:11??test?images\ukbench00082.jpg
?????文件??????162436??2014-03-16?05:48??test?images\ukbench00082.jpg.dsift
............此處省略13個(gè)文件信息