生成使用mean shift算法进行图像聚类分割的matlab代码

以下是使用mean shift算法进行图像聚类分割的matlab代码：

clc;clear;close all;

% 读取图像
I = imread('test.jpg');
figure,imshow(I);

% 将图像转化为二维矩阵
[m,n,k] = size(I);
X = double(reshape(I,m*n,k));

% 计算带宽
bandwidth = 30;

% 设置停止条件
tol = 1e-3;

% 计算mean shift向量
ms = ms_func(X,bandwidth,tol);

% 计算聚类结果
[clustCent,data2cluster,cluster2dataCell] = MeanShiftCluster(X',bandwidth,ms);
numClust = length(cluster2dataCell);

% 显示聚类结果
figure,hold on
colors = 'rgbk';
for i = 1:numClust
    index = cluster2dataCell{i};
    scatter3(X(index,1),X(index,2),X(index,3),35,colors(mod(i,4)+1));
end
hold off

其中，ms_func函数如下所示：

function ms = ms_func(X,bandwidth,tol)

% 初始化mean shift向量
ms = zeros(size(X));

% 开始迭代
for i = 1:size(X,1)
    x = X(i,:)';
    prev_x = x;
    diff = inf;
    iter = 0;
    while norm(diff) > tol
        % 计算当前点的mean shift向量
        [idx, dist] = rangesearch(X,x',bandwidth);
        idx = idx{1};
        weight = exp(-(dist{1}/bandwidth).^2);
        ms_x = sum(bsxfun(@times,X(idx,:),weight),1)/sum(weight);
        
        % 计算与上一个点的差异
        diff = ms_x' - x;
        x = ms_x';
        
        % 更新迭代次数
        iter = iter + 1;
        if iter > 100
            break;
        end
    end
    
    % 保存mean shift向量
    ms(i,:) = x;
end
end

MeanShiftCluster函数如下所示：

function [clustCent,data2cluster,cluster2dataCell] = MeanShiftCluster(dataPts,bandwidth,ms)

% 获取数据点数量
numPoints = size(dataPts,2);

% 初始化标记矩阵
beenVisitedFlag = zeros(1,numPoints,'uint8');
clusterIndex = 1;
numClusters = 0;
clusterVotes = zeros(1,numPoints,'uint16');

% 开始聚类
while (sum(beenVisitedFlag) < numPoints)
    % 找到未被访问的点
    index = find(beenVisitedFlag == 0,1);
    thisClusterVotes = zeros(1,numPoints,'uint16');
    
    % 找到该点的簇
    while true
        % 计算当前点与所有点的距离
        dists = sqrt(sum(bsxfun(@minus,ms(index,:),ms).^2,2));
        
        % 找到与当前点距离小于等于带宽的点
        inCluster = find(dists <= bandwidth);
        
        % 统计该簇的投票数
        thisClusterVotes(inCluster) = thisClusterVotes(inCluster) + 1;
        
        % 更新已访问标记
        beenVisitedFlag(inCluster) = 1;
        
        % 如果该簇中的点数量大于等于3，则认为该簇合法
        if length(inCluster) >= 3
            break;
        end
        
        % 如果该簇中的点数量小于3，则找到最近的点，继续迭代
        idx = find(beenVisitedFlag == 0);
        dists = dists(idx);
        [minDist,loc] = min(dists);
        index = idx(loc);
    end
    
    % 更新聚类中心
    clustCent(clusterIndex,:) = mean(ms(inCluster,:),1);
    
    % 记录该簇的投票数
    clusterVotes = clusterVotes + thisClusterVotes;
    
    % 将该簇的所有点加入簇集合中
    data2cluster(inCluster) = clusterIndex;
    
    % 更新簇集合
    cluster2dataCell{clusterIndex} = inCluster;
    numClusters = numClusters + 1;
    clusterIndex = clusterIndex + 1;
end
end

运行代码后，将生成聚类后的图像。