LSH、ITQ、SKLSH图像检索实验实现(包含源码下载地址)

原文来自我的独立blog：http://www.yuanyong.org/blog/cv/lsh-itq-sklsh-compliment

这两天寻找idea，想来思去也没想到好点的方法，于是把前段时间下过来的一篇《Iterative Quantization: A Procrustean Approach to Learning Binary Codes》和代码拿出来又细读了一番，希望可以从中获得点启发。

Iterative Quantization: A Procrustean Approach to Learning Binary Codes的Project请戳这里，一作是Yunchao Gong，师从Svetlana Lazebnik，第一次听说Svetlana Lazebnik是一次在提取图像特征时立超师兄说可以用Spatial Pyramid特征，说Svetlana Lazebnik自信得直接是”Beyond Bags of Features: ……”（超越BOW），然后下去看了一下大牛的homePage，所以对Svetlana Lazebnik有些印象。扯远了，回归正题。代码下载下来后，发觉paper里做好的数据库并没提供，有需要请戳这里下载：代码与数据库。解压文件，比较重要的有三个，cca.m、ITQ.m、RF_train.m、recall_precision.m。

实验评价方案recall_precision.m跟我之前用过的一个评价方案不同，花了大半个下午，85%的弄明白了这个recall_precision.m评价方案。先理理一下已经完全整明白了的LSH吧。

表1

表1是对不同编码方法的说明，从表中可以看出LSH的哈希函数是  sgn(wTx+b)  。实际上，对于采用哈希方法做检索，分两个阶段：(1). 投影阶段(Projection Stage); (2). 量化阶段(Quantization Stage)。LSH投影矩阵(即哈希系列函数)是随机产生的。Matlab中生成投影矩阵为: w=randn(size(X,2), bit),  X∈Rn×d  ，bit是编码位数，  g1↔w1=w(:,1)  ，  g2↔w2=w(:,2)  ，···，  gn↔wn=w(:,L)  。对于  xx  ，通过  w  投影后经过阈值化(量化)后映射到哈希桶中。

1. 对于原空间数据，对于进行中心化(centre the data)后在量化阶段阈值变为0，整个压缩编码的代码为：

1	XX = XX * randn(size(XX,2),bit);

2	Y = zeros(size(XX));

3	Y(XX>=0)=1;         %原数据中心化后，阈值设为0。大于0编码为1，小于0编码为0

4	Y = compactbit(Y);  %将二进制用十进制表示

表1中对于LSH的投影过程(Projection)是数据独立(data-independent)，对于data-independent，[2]中指出”Generally,data-independent methods need longer codes than data-dependent methods to achieve satisfactory performance”，即要获得比数据非独立方法一样满意的表现，数据独立方法需要更长的编码。

1	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

2	Function: this is a geometric illustration of Draw the recall precision curve

3	%Author: Willard (Yuan Yong' English Name)%Date: 2013-07-22

4	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

5	close all;

6	clear variables;

7

clc;

8	load cifar_10yunchao.mat;

9	X=cifar10;

10	X=X(:,1:end-1);

11

12	%Get the recall & precision

13	[recall{1,1}, precision{1,1}] = test(X, 16, 'LSH');

14	[recall{1,2}, precision{1,2}] = test(X, 24, 'LSH');

15	[recall{1,3}, precision{1,3}] = test(X, 32, 'LSH');

16	[recall{1,4}, precision{1,4}] = test(X, 64, 'LSH');

17	[recall{1,5}, precision{1,5}] = test(X, 128, 'LSH');

18

19	% Draw the plot

20	figure; hold on;grid on;

21	plot(recall{1, 1}, precision{1, 1},'g-^','linewidth',2);

22	plot(recall{1, 2}, precision{1, 2},'b-s','linewidth',2);

23	plot(recall{1, 3}, precision{1, 3},'k-p','linewidth',2);

24	plot(recall{1, 4}, precision{1, 4},'m-d','linewidth',2);

25	plot(recall{1, 5}, precision{1, 5},'r-o','linewidth',2);

26	xlabel('Recall');

27	ylabel('Precision');

28	legend('LSH-16 bit','LSH-24 bit','LSH-32 bit','LSH-64 bit','LSH-128 bit','Location','NorthEast');

图1

从图1可以看出，PCA-ITQ比PCA-RR、LSH、SKLSH表现性能要佳。ITQ的代码还在分析中，希望可以从中获得点启示。

2. PCA-ITQ, PCA-RR, LSH, SKLSH对比实验

1	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

2	% Function: this is a geometric illustration of Draw the recall precision curve

3	%Author: Willard (Yuan Yong' English Name)

4	%Date: 2013-07-22

5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

6

7	close all;

8	clear variables;

9

clc;

10	load cifar_10yunchao.mat;

11	X=cifar10;

12	X=X(:,1:end-1);

13

bit=32;

14

15	%Get the recall & precision

16	[recall{1,1}, precision{1,1}] = test(X, bit, 'ITQ');

17	[recall{1,2}, precision{1,2}] = test(X, bit, 'RR');

18	[recall{1,3}, precision{1,3}] = test(X, bit, 'LSH');

19	[recall{1,4}, precision{1,4}] = test(X, bit, 'SKLSH');

20

21	% Draw the plot

22	figure; hold on;grid on;

23	plot(recall{1, 1}, precision{1, 1},'r-o','linewidth',2);

24	plot(recall{1, 2}, precision{1, 2},'b-s','linewidth',2);

25	plot(recall{1, 3}, precision{1, 3},'k-p','linewidth',2);

26	plot(recall{1, 4}, precision{1, 4},'m-d','linewidth',2);

27	plot(recall{1, 5}, precision{1, 5},'g-^','linewidth',2);

28	xlabel('Recall');ylabel('Precision');legend('PCA-ITQ','PCA-RR','LSH','SKLSH','Location','NorthEast');

                                                                                  图2
3. PCA-ITQ检索实例实验主要代码：

1	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

2	% Function: this is a PCA-ITQ demo showing the retrieval sample

3	%Author: Willard (Yuan Yong' English Name)

4	%Date: 2013-07-23

5	%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

6

7	clear all;

8

clc;

9

10	% parameters

11

bit=32;

12	averageNumberNeighbors = 50;   % ground truth is 50 nearest neighbor

13	num_test = 1000;               % 1000 query test point, rest are database

14	load cifar_10yunchao.mat;

15	[ndata, D] = size(cifar10);

16	Xtraining= double(cifar10(1:59000,1:end-1));

17	Xtest = double(cifar10(59001:60000,1:end-1));

18	num_training = size(Xtraining,1);

19

20	% generate training ans test split and the data matrix

21	XX = [Xtraining; Xtest];

22	% center the data, VERY IMPORTANT

23	sampleMean = mean(XX,1);

24	XX = (double(XX)-repmat(sampleMean,size(XX,1),1));

25

26

% PCA

27	[pc, l] = eigs(cov(XX(1:num_training,:)),bit);

28	XX = XX * pc;

29

30

% ITQ

31	[Y, R] = ITQ(XX(1:num_training,:),50);

32	XX = XX*R;

33	Y = zeros(size(XX));

34	Y(XX>=0) = 1;

35	Y = compactbit(Y>0);

36

37	% compute Hamming metric and compute recall precision

38	B1 = Y(1:size(Xtraining,1),:); %编码后的训练样本

39	B2 = Y(size(Xtraining,1)+1:end,:);%编码后的测试样本

40	Dhamm = hammingDist(B2, B1);

41	[foo, Rank] = sort(Dhamm, 2,'ascend'); %foo为汉明距离按每行由小到大排序

42

43	% show retrieval images

44	load cifar-10-batches-mat/data_batch_1.mat;

45	data1=data;

46	labels1=labels;

47	clear data labels;

48	load cifar-10-batches-mat/data_batch_2.mat;

49	data2=data;

50	labels2=labels;

51	clear data labels;

52	load cifar-10-batches-mat/data_batch_3.mat;

53	data3=data;

54	labels3=labels;

55	clear data labels;

56	load cifar-10-batches-mat/data_batch_4.mat;

57	data4=data;

58	labels4=labels;

59	clear data labels;

60	load cifar-10-batches-mat/data_batch_5.mat;

61	data5=data;

62	labels5=labels;

63	clear data labels;

64	load cifar-10-batches-mat/test_batch.mat;

65	data6=data;

66	labels6=labels;

67	clear data labels;

68	database=[data1 labels1 ;data2 labels2;data3 labels3;data4 labels4;data5 labels5;data6 labels6];

69	cifar10labels=[labels1;labels2;labels3;labels4;labels5;labels6];

70	%save('./data/cifar10labels.mat','cifar10labels');

71	%index=[50001,Rank(1,1:129)]'; P001是猫

72	%index=[50002,Rank(2,1:129)]'; P002是船

73	%index=[59004,Rank(4,1:129)]'; Y004是猫

74	%index=[59005,Rank(5,1:129)]'; %马

75	%index=[59006,Rank(6,1:129)]'; %狗

76	%index=[59018,Rank(18,1:129)]'; % 飞机

77	index=[59018,Rank(18,1:129)]'; % 飞机

78	%index=[50007,Rank(7,1:129)]'; P007是automobile

79

rank=1;

80	left=0.005;

81	botton=0.895;

82	width=0.08;

83	height=0.08;

84

85	% show the retrieved images

86	for i=1:130

87	j=index(i,1);

88	image1r=database(j,1:1024);

89	image1g=database(j,1025:2048);

90	image1b=database(j,2049:end-1);

91	image1rr=reshape(image1r,32,32);

92	image1gg=reshape(image1g,32,32);

93	image1bb=reshape(image1b,32,32);

94	image1(:,:,1)=image1rr';

95	image1(:,:,2)=image1gg';

96	image1(:,:,3)=image1bb';

97	image1=uint8(image1);

98	if(mod(rank,13)~=0)

99	hdl1=subplot(10,13,rank,'position',[left+0.07*(mod(rank,13)-1) botton-0.09*fix(rank/13) width height]);

100	imshow(image1);

101

else

102	hdl1=subplot(10,13,rank,'position',[left+0.07*12 botton-0.09*fix(rank/14) width height]);

103	imshow(image1);

104

end

105	rank=rank+1;

106

end

第1幅是查询图像，后面129是从59k的database里检索出来的相似的图像。

enjoy yourself~