matlab C均值聚类算法FCM用图像分割的彻底解析

时间：2024-10-14 06:50:55

1、首先，你要知道限鬃鹣哺什么是C均值聚类算法，就是那个公式，你最好要能推出来，其次，要明白matlab中自带僻棍募暖FCM 的代码含义，在命令窗中输入 edit fcm; 会在M文件中打开，前面是注释function [center, U, obj_fcn] = fcm(data, cluster_n, options)%FCM Data set clustering using fuzzy c-means clustering.%% [CENTER, U, OBJ_FCN] = FCM(DATA, N_CLUSTER) findsN_CLUSTER number of% clusters in the data set DATA. DATA is size M-by-N, where M isthe number of% data points and N is the number of coordinates for each data point. The% coordinates for each cluster center are returned in the rows of the matrix% CENTER. The membership function matrix U contains the grade of membership of% each DATA point in each cluster. The values 0 and 1 indicate no membership% and full membership respectively. Grades between 0 and 1 indicate that the% data point has partial membership in a cluster. At each iteration, an% objective function is minimized to find the best location for the clusters% and its values are returned in OBJ_FCN.%% [CENTER, ...] = FCM(DATA,N_CLUSTER,OPTIONS) specifies a vector of options% for the clustering process:% OPTIONS(1): exponent for the matrix U (default: 2.0)% OPTIONS(2): maximum number of iterations (default: 100)% OPTIONS(3): minimum amount of improvement (default: 1e-5)% OPTIONS(4): info display during iteration (default: 1)% The clustering process stops when the maximum number of iterations% is reached, or when the objective function improvement between two% consecutive iterations is less than the minimum amount of improvement% specified. Use NaN to select the default value.%% Example% data = rand(100,2);% [center,U,obj_fcn] = fcm(data,2);% plot(data(:,1), data(:,2),'o');% hold on;% maxU = max(U);% % Find the data points with highest grade of membership in cluster 1% index1 = find(U(1,:) == maxU);% % Find the data points with highest grade of membership in cluster 2% index2 = find(U(2,:) == maxU);% line(data(index1,1),data(index1,2),'marker','*','color','g');% line(data(index2,1),data(index2,2),'marker','*','color','r');% % Plot the cluster centers% plot([center([1 2],1)],[center([1 2],2)],'*','color','k')% hold off;%% See also FCMDEMO, INITFCM, IRISFCM, DISTFCM, STEPFCM.% Roger Jang, 12-13-94, N. Hickey 04-16-01% Copyright 1994-2002 The MathWorks, Inc.% $Revision: 1.13 $ $Date: 2002/04/14 22:20:38 $% %后是说明部分，从此处开始是函数定义if nargin ~= 2 & nargin ~= 3,error('Too many or too few input arguments!');enddata_n = size(data, 1);in_n = size(data, 2);% Change the following to set default optionsdefault_options = [2;% exponent for the partition matrix U100;% max. number of iteration1e-5;% min. amount of improvement1];% info display during iterationif nargin == 2,options = default_options;else% If "options" is not fully specified, pad it with default values.if length(options) < 4,tmp = default_options;tmp(1:length(options)) = options;options = tmp;end% If some entries of "options" are nan's, replace them with defaults.nan_index = find(isnan(options)==1);options(nan_index) = default_options(nan_index);if options(1) <= 1,error('The exponent should be greater than 1!');endendexpo = options(1);% Exponent for Umax_iter = options(2);% Max. iterationmin_impro = options(3);% Min. improvementdisplay = options(4);% Display info or notobj_fcn = zeros(max_iter, 1);% Array for objective functionU = initfcm(cluster_n, data_n);% Initial fuzzy partition% Main loopfor i = 1:max_iter,[U, center, obj_fcn(i)] = stepfcm(data, U, cluster_n, expo);if display,fprintf('Iteration count = %d, obj. fcn = %f\n', i, obj_fcn(i));end% check termination conditionif i > 1,if abs(obj_fcn(i) - obj_fcn(i-1)) < min_impro, break; end,endenditer_n = i;% Actual number of iterationsobj_fcn(iter_n+1:max_iter) = [];英文看起来比较郁闷的看中文如下