• 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~
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  • 原文地址：https://www.cnblogs.com/keanuyaoo/p/3331267.html
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