其他分享
首页 > 其他分享> > 山东大学机器学习实验六 K-Means

山东大学机器学习实验六 K-Means

作者：互联网

山东大学机器学习实验6报告

实验学时： 4 实验日期：2021.11.29

文章目录

实验题目：Experiment 6 : K-Means

实验目的

In this exercise, you will use K-means to compress an image by reducing the
number of colors it contains

使用K-means 通过减少颜色的数量来压缩图像

实验环境

软件环境

​ Windows 10 + Matlab R2020a

​ CPU: Intel® Core™ i5-8300H CPU @ 2.30GHz 2.30 GHz

实验步骤与内容

了解K-Means

K-Means (Lloyd 1957)

  1. 更新每个点的Class K: 通过计算每个数据点 X i X_i Xi​ 到 每个类别中心点 μ k \mu_k μk​的距离，来选择某点属于哪个类别

    image-20211129171043582

  2. 更新每个cluster means : 即根据当前这个类别的点，进行求mean操作，来得到中心点

    image-20211129171255792

  3. 当 μ k \mu_k μk​或者Loss 不变时，认为迭代完成。

    Loss:

    image-20211129171436963

Loss Function

μ k \mu_k μk​认为是第K个聚类的质心， z i , k z_{i,k} zi,k​认为是 x i x_i xi​是否属于 C k C_k Ck​的一个指示，然后对于每一条数据都会有一个 z i z_i zi​，因此定义某个数据 x i x_i xi​的 l o s s loss loss为：

image-20211129172209598

总的 L o s s F u n c t i o n : Loss Function: LossFunction: 其中 X   i s   N × D   X \, is \, N \times D\, XisN×D 然后 Z   i s   N × K 且 μ   i s   K × D Z\, is \, N \times K 且 \mu \, is \, K \times D ZisN×K且μisK×D

image-20211129172334281

意思就是X表示所有D维数据，Z表示所有N个数据的聚类类别指示， μ \mu μ表示K个聚类的D维中心点。K-Means 就是去最小化这个Loss Function

K-Means Objective

​ K-Means 是一个启发式的方法，来解决这个NP-hard问题，并且他是一个Non-Convex problem，存在很多local minima

而算法描述为：

image-20211129173342945

Choosing K

​ 选择聚类的个数也是一个问题。

Task Description:

​ Your task is to compute 16 cluster centroids from this image, with each centroid being a vector of length three that holds a set of RGB values.

​ 计算16个聚类，每一个中心点都是一个 s i z e = 3 × 1 size = 3 \times 1 size=3×1的RGB p i x e l pixel pixel像素点 v a l u e value value表示RGB的值

​ 鉴于计算 538 × 538 × 3 538 \times 538 \times 3 538×538×3的图片的中心点会耗时巨大，因此先在 小图片 ( 128 × 128 × 3 ) (128\times128\times3) (128×128×3)训练完之后，应用在大图片上，得到一个只使用16个color就表示的新图片

Experimental steps :

K-Means Algorithm

  1. 随机初始化： 从整个picture中随机选取16个pixel 作为初始化的迭代中心点。我采取的随机数方案可以实现不重复采样。

    function code:

    %% 随机选取初始点 return K * 3 
function sample_Datas = sample_random(num,datas,dimx,dimy)
    % datas 为原始数据 num 为目标数目 
    sample_Datas = zeros(num,3);
    a = rand(dimx,1);
    b = rand(dimy,1);
    [vala,posa] = sort(a);
    [valb,posb] = sort(b);
    chose_x = posa(1:num,1);
    chose_y = posb(1:num,1);
    for i=1:num
        sample_Datas(i,:) = datas(chose_x(i),chose_y(i),:);
    end
end

  2. 计算每个Pixel的最邻近点： 遍历每个像素，每个pixel都有一个 R G B   v e c t o r RGB \, vector RGBvector 并且 s i z e = 3 × 1 size = 3\times 1 size=3×1 然后和 所有中心点求距离，选最小距离对应的中心点的K 作为这个pixel的class

    image-20211129213325077

    function code:

    %% 计算每个pixel 的 类别 return Clusters: N * N val 为 类别 
function Clusters = calculate_Ci(centroids,dimx,dimy,datas,K)
    Clusters = [];
    % 遍历每个pixel 计算一个z(i,j)
    for i= 1:dimy
        for j = 1:dimx
            % 得到xi
            pixel_rgb = [datas(i,j,1),datas(i,j,2),datas(i,j,3)];
            diff = ones(K,1)*pixel_rgb - centroids;
            distance = sum(diff.^2,2);
            [~,class] = min(distance); % 得到最小的对应的类别的index
            Clusters(i,j) = class;
        end
    end
end

  3. 更新中心点 μ \mu μ 通过

image-20211129213332139

​ Function code:

​

%% 更新中心点 return 16 * 3
function newcenters = updatemeans(Clusters,dimx,dimy,dimz,datas,K)
    newcenters = zeros(K,dimz);
    nums =  zeros(K);
    sumred = zeros(K,1);
    sumgreen = zeros(K,1);
    sumblue = zeros(K,1);
    for i=1:dimx
        for j=1:dimy
            class = Clusters(i,j);
            nums(class) = nums(class) + 1;
            sumred(class) = sumred(class) + datas(i,j,1);
            sumgreen(class) = sumgreen(class) + datas(i,j,2);
            sumblue(class) = sumblue(class) + datas(i,j,3);
        end
    end
    for i=1:K
        if nums(i) ~= 0
            sumred(i) = sumred(i) / nums(i);
            sumgreen(i) = sumgreen(i) / nums(i);
            sumblue(i) = sumblue(i) / nums(i);
        end
    end
    newcenters = [sumred,sumgreen,sumblue];
    newcenters = round(newcenters);
end

  1. 判断收敛：

    我设定了最大收敛次数为150次，或者根据所有中心点更新前后的平方之和如果< 1 0 − 5 10^{-5} 10−5就认为是没变化了，收敛了。

编程实现上述过程，最后得到 每个像素点对应的** C l u s t e r s , s i z e = N × N Clusters,size = N \times N Clusters,size=N×N** 以及 每个中心点的RGB值： C e n t r o i d s , s i z e = K ∗ 3 Centroids,size = K*3 Centroids,size=K∗3

Reassigning Colors to The Large Image

​ 在这里，我分别对小图和大图都进行了处理。

​ 小图片：即对每个pixel 转化为 对应的class 的 RGB 值

​ 大图片：首先计算每个pixel 和 训练出来的中心点的最临近点，然后更换RGB值，得到新图片。

​ 随机一次初始值的效果：

​ 小图片：

small

大图片：分辨率更高

large
Choosing A Good Initial Point

选择一个好的初始点，为此我对上述过程进行循环，设定次数。意在选一个比较好的初始点。

每次计算出最后的图片之后，与原图进行逐一pixel的rgb差值求解，然后对RGB进行平方均值，计算出Loss，得到最低loss 的图片

作为Best New Small / Large Image

效果如下：

小图片：

Best New Small Image

大图片：

Best New Large Image

与原图对比一下，效果不错：

image-20211129215346168

同时记录以下Loss 和 不同次进行随机初始的关系，画出图表：

可以看出：整体效果与初始点的初始有关系，可能存在Local Mininum

image-20211130191341135image-20211130191356956

结论分析与体会

K-Means 整体算法架构

​ 即：image-20211129215916813

​ 每次固定中心点，然后更新每个点的类别

​ 或者固定每个点的类别，据此来更新中心点位置信息

Some Limitations Of K-Means

K-Means 具有适用范围：

同时在non-convex shaped 表现不好

Kernel K-Means

即进行高维映射，以此来解决non-convex问题。同时仍不会显式投影，而是用 K K K

Hierarchical Clustering

层次聚类，bottom-up、top-down

实验源代码

Lab61.m

clear,clc
%% K-means
% small_img : 128 * 128 * 3
% large_img : 538 * 538 * 3
small_img = imread('bird_small.tiff');
large_img = imread('bird_large.tiff');
% 转化为double 矩阵 A(50,33,3) 表示 y = 50 x =33 的Blue 的val
small_img_matrix = double(small_img);
large_img_matrix = double(large_img);
%% show image 
% imshow(large_img)
%%
K = 16; % 16个class
[small_dimx,small_dimy,small_dimz] = size(small_img_matrix);
[large_dimx,large_dimy,large_dimz] = size(large_img_matrix);
max_iter_times = 200;
convergence_condition = 10^-5;
[centroids,Clusters] = kmeans(K,small_img_matrix,max_iter_times,convergence_condition);
%% 重新展示小图片
new_small_pic=[];
new_large_pic=[];
% 小图片处理
for i=1:small_dimx
    for j=1:small_dimy
        new_small_pic(i,j,:) =  centroids(Clusters(i,j),:);
    end
end

% 大图片处理
for i=1:large_dimy
    for j=1:large_dimy
        pixel_rgb = [large_img_matrix(i,j,1),large_img_matrix(i,j,2),large_img_matrix(i,j,3)];
        diff = ones(K,1)*pixel_rgb - centroids;
        distance = sum(diff.^2,2);
        [~,class] = min(distance); % 得到类别
        new_large_pic(i,j,:) =  centroids(class,:);
    end
end
%% 展示小图片 和 大图片
figure
subplot(2,1,1);
imshow(uint8(small_img_matrix)),title('Origin Small Image');
subplot(2,1,2);
imshow(uint8(new_small_pic)),title('New Small Image')
%% 
figure
subplot(2,1,1);
imshow(uint8(large_img_matrix)),title('Origin Large Image');
subplot(2,1,2);
imshow(uint8(new_large_pic)),title('New Large Image')
%% K-Means Fuction Return ?
function [centroids,Clusters] = kmeans(K,datas,max_iter_times,convergence_condition)
    % 返回中心点 K * dimz
    centroids = [];
    [dimy,dimx,dimz] = size(datas);
    % 随机初始化中心点 K*3
    centroids = sample_random(K,datas,dimx,dimy);
    for it = 1 : max_iter_times
        % 计算its nearest mean
        Clusters = calculate_Ci(centroids,dimx,dimy,datas,K);
        % 更新中心点
        new_centroids = updatemeans(Clusters,dimx,dimy,dimz,datas,K);
        % 看是否收敛
        convergence = judge(new_centroids,centroids,convergence_condition);
        centroids = new_centroids;
        if convergence
            break
        end
    end
end

%% 随机选取初始点 return K * 3 
function sample_Datas = sample_random(num,datas,dimx,dimy)
    % datas 为原始数据 num 为目标数目 
    sample_Datas = zeros(num,3);
    a = rand(dimx,1);
    b = rand(dimy,1);
    [vala,posa] = sort(a);
    [valb,posb] = sort(b);
    chose_x = posa(1:num,1);
    chose_y = posb(1:num,1);
    for i=1:num
        sample_Datas(i,:) = datas(chose_x(i),chose_y(i),:);
    end
    
end

%% 计算每个pixel 的 类别 return Clusters: N * N val 为 类别 
function Clusters = calculate_Ci(centroids,dimx,dimy,datas,K)
    Clusters = [];
    % 遍历每个pixel 计算一个z(i,j)
    for i= 1:dimy
        for j = 1:dimx
            % 得到xi
            pixel_rgb = [datas(i,j,1),datas(i,j,2),datas(i,j,3)];
            diff = ones(K,1)*pixel_rgb - centroids;
            distance = sum(diff.^2,2);
            [~,class] = min(distance); % 得到最小的对应的类别的index
            Clusters(i,j) = class;
        end
    end
end

%% 更新中心点 return 16 * 3
function newcenters = updatemeans(Clusters,dimx,dimy,dimz,datas,K)
    newcenters = zeros(K,dimz);
    nums =  zeros(K);
    sumred = zeros(K,1);
    sumgreen = zeros(K,1);
    sumblue = zeros(K,1);
    for i=1:dimx
        for j=1:dimy
            class = Clusters(i,j);
            nums(class) = nums(class) + 1;
            sumred(class) = sumred(class) + datas(i,j,1);
            sumgreen(class) = sumgreen(class) + datas(i,j,2);
            sumblue(class) = sumblue(class) + datas(i,j,3);
        end
    end
    for i=1:K
        if nums(i) ~= 0
            sumred(i) = sumred(i) / nums(i);
            sumgreen(i) = sumgreen(i) / nums(i);
            sumblue(i) = sumblue(i) / nums(i);
        end
    end
    newcenters = [sumred,sumgreen,sumblue];
    newcenters = round(newcenters);
end

%% 判断是否收敛
function convergence = judge(newcenter,oldcenter,condition)
    convergence = 0;
     d = sum(sqrt(sum((newcenter - oldcenter).^2, 2)));
    if d < condition
        convergence = 1;
    end
end

%% 返回行列坐标
function [row,col] = findrc(Clusters,val)
    [dimx,dimy] = size(Clusters);
    row = [];
    col = [];
    for i=1:dimx
        for j=1:dimy
            if Clusters(i,j) == val
                row = [row;i];
                col = [col;j];
            end
        end
    end
end

Lab62.m (多次迭代选最好效果)

clear,clc
%% 多进行几次，目的是初始点不同，找一个效果最好的。
%% K-means
% small_img : 128 * 128 * 3
% large_img : 538 * 538 * 3
small_img = imread('bird_small.tiff');
large_img = imread('bird_large.tiff');
% 转化为double 矩阵 A(50,33,3) 表示 y = 50 x =33 的Blue 的val
small_img_matrix = double(small_img);
large_img_matrix = double(large_img);
Best_new_small_pic=[];
Best_new_large_pic=[];
%% show image 
% imshow(large_img)
%%
inital_times = 10;
for inital_time = 1 : inital_times
    K = 16; % 16个class
    [small_dimx,small_dimy,small_dimz] = size(small_img_matrix);
    [large_dimx,large_dimy,large_dimz] = size(large_img_matrix);
    max_iter_times = 200;
    convergence_condition = 10^-5;
    [centroids,Clusters] = kmeans(K,small_img_matrix,max_iter_times,convergence_condition);
    %% 重新展示小图片
    loss_small = 1e9;
    loss_large = 1e9;
    new_small_pic=[];
    new_large_pic=[];
    % 小图片处理
    for i=1:small_dimx
        for j=1:small_dimy
            new_small_pic(i,j,:) =  centroids(Clusters(i,j),:);
        end
    end
    new_loss_small = calculate_Kmeans_Loss(small_img_matrix,new_small_pic,small_dimx,small_dimy);
    if new_loss_small < loss_small
        loss_small = new_loss_small;
        Best_new_small_pic = new_small_pic;
    end

    % 大图片处理
    for i=1:large_dimy
        for j=1:large_dimx
            pixel_rgb = [large_img_matrix(i,j,1),large_img_matrix(i,j,2),large_img_matrix(i,j,3)];
            diff = ones(K,1)*pixel_rgb - centroids;
            distance = sum(diff.^2,2);
            [~,class] = min(distance); % 得到类别
            new_large_pic(i,j,:) =  centroids(class,:);
        end
    end
    
    new_loss_large = calculate_Kmeans_Loss(large_img_matrix,new_large_pic,large_dimy,large_dimx);
    if new_loss_large < loss_large
        loss_large = new_loss_large;
        Best_new_large_pic = new_large_pic;
    end
end
%% 展示小图片 和 大图片
figure
subplot(2,1,1);
imshow(uint8(small_img_matrix)),title('Origin Small Image');
subplot(2,1,2);
imshow(uint8(Best_new_small_pic)),title('Best New Small Image')
imwrite(uint8(Best_new_small_pic),'Best New Small Image.png')

figure
subplot(2,1,1);
imshow(uint8(large_img_matrix)),title('Origin Large Image');
subplot(2,1,2);
imshow(uint8(Best_new_large_pic)),title('Best New Large Image')
imwrite(uint8(Best_new_large_pic),'Best New Large Image.png')
%% K-Means Fuction Return ?
function [centroids,Clusters] = kmeans(K,datas,max_iter_times,convergence_condition)
    % 返回中心点 K * dimz
    centroids = [];
    [dimy,dimx,dimz] = size(datas);
    % 随机初始化中心点 K*3
    centroids = sample_random(K,datas,dimx,dimy);
    for it = 1 : max_iter_times
        % 计算its nearest mean
        Clusters = calculate_Ci(centroids,dimx,dimy,datas,K);
        % 更新中心点
        new_centroids = updatemeans(Clusters,dimx,dimy,dimz,datas,K);
        % 看是否收敛
        convergence = judge(new_centroids,centroids,convergence_condition);
        centroids = new_centroids;
        if convergence
            break
        end
    end
end

%% 随机选取初始点 return K * 3 
function sample_Datas = sample_random(num,datas,dimx,dimy)
    % datas 为原始数据 num 为目标数目 
    sample_Datas = zeros(num,3);
    a = rand(dimx,1);
    b = rand(dimy,1);
    [vala,posa] = sort(a);
    [valb,posb] = sort(b);
    chose_x = posa(1:num,1);
    chose_y = posb(1:num,1);
    for i=1:num
        sample_Datas(i,:) = datas(chose_x(i),chose_y(i),:);
    end
    
end

%% 计算每个pixel 的 类别 return Clusters: N * N val 为 类别 
function Clusters = calculate_Ci(centroids,dimx,dimy,datas,K)
    Clusters = [];
    % 遍历每个pixel 计算一个z(i,j)
    for i= 1:dimy
        for j = 1:dimx
            % 得到xi
            pixel_rgb = [datas(i,j,1),datas(i,j,2),datas(i,j,3)];
            diff = ones(K,1)*pixel_rgb - centroids;
            distance = sum(diff.^2,2);
            [~,class] = min(distance); % 得到最小的对应的类别的index
            Clusters(i,j) = class;
        end
    end
end

%% 更新中心点 return 16 * 3
function newcenters = updatemeans(Clusters,dimx,dimy,dimz,datas,K)
    newcenters = zeros(K,dimz);
    nums =  zeros(K);
    sumred = zeros(K,1);
    sumgreen = zeros(K,1);
    sumblue = zeros(K,1);
    for i=1:dimx
        for j=1:dimy
            class = Clusters(i,j);
            nums(class) = nums(class) + 1;
            sumred(class) = sumred(class) + datas(i,j,1);
            sumgreen(class) = sumgreen(class) + datas(i,j,2);
            sumblue(class) = sumblue(class) + datas(i,j,3);
        end
    end
    for i=1:K
        if nums(i) ~= 0
            sumred(i) = sumred(i) / nums(i);
            sumgreen(i) = sumgreen(i) / nums(i);
            sumblue(i) = sumblue(i) / nums(i);
        end
    end
    newcenters = [sumred,sumgreen,sumblue];
    newcenters = round(newcenters);
end

%% 判断是否收敛
function convergence = judge(newcenter,oldcenter,condition)
    convergence = 0;
     d = sum(sqrt(sum((newcenter - oldcenter).^2, 2)));
    if d < condition
        convergence = 1;
    end
end

%% 计算K-Means 之后的图片的loss：计算所有pixel的rgb sum 然后 avg
function Loss1 = calculate_Kmeans_Loss(ori_img,new_img,dimx,dimy)
    Loss = zeros(3,1);
    div_num = dimx*dimy;
    for i=1:dimx
        for j = 1 :dimy
            Loss(1) = Loss(1) + (ori_img(i,j,1)-new_img(i,j,1)).^2 / div_num;
            Loss(2) = Loss(2) + (ori_img(i,j,2)-new_img(i,j,2)).^2 / div_num;
            Loss(3) = Loss(3) + (ori_img(i,j,3)-new_img(i,j,3)).^2 / div_num;
        end
    end
    Loss1 = sum(Loss) / 3;
end

n

    convergence = 1;
end

end

%% 计算K-Means 之后的图片的loss：计算所有pixel的rgb sum 然后 avg
function Loss1 = calculate_Kmeans_Loss(ori_img,new_img,dimx,dimy)
Loss = zeros(3,1);
div_num = dimx*dimy;
for i=1:dimx
for j = 1 :dimy
Loss(1) = Loss(1) + (ori_img(i,j,1)-new_img(i,j,1)).^2 / div_num;
Loss(2) = Loss(2) + (ori_img(i,j,2)-new_img(i,j,2)).^2 / div_num;
Loss(3) = Loss(3) + (ori_img(i,j,3)-new_img(i,j,3)).^2 / div_num;
end
end
Loss1 = sum(Loss) / 3;
end

标签：机器,img,Means,large,small,end,dimy,山东大学,datas
来源： https://blog.csdn.net/qq_47865838/article/details/121640496