LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址)

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。

LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址)

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');

LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址)

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');

LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址) 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。

LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址)

enjoy yourself~

LSH、ITQ、SKLSH图像检索实验实现(包孕源码下载地址)

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