bayesgauss.m

冈萨雷斯的经典教材数字图像处理的matlab版源代码

function d = bayesgauss(X, CA, MA, P)
%BAYESGAUSS Bayes classifier for Gaussian patterns.
%   D = BAYESGAUSS(X, CA, MA, P) computes the Bayes decision
%   functions of the n-dimensional patterns in the rows of X. 
%   CA is an array of size n-by-n-by-W containing W covariance
%   matrices of size n-by-n, where W is the number of classes.
%   MA is an array of size n-by-W, whose columns are the corres-
%   ponding mean vectors. A cov. matrix and a mean vector must be 
%   specified for each class, even is some are equal.  X is of size 
%   K-by-n, where K is the number of patterns to be classified. P is 
%   a 1-by-W array, containing the probabilities of occurrence of 
%   each class.  If P is not included in the argument, the classes 
%   are assumed to be equally likely.  
%
%   D, is a column vector of length K. Its ith element is the class
%   number assigned to the ith vector in X during classification.  

%   Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins
%   Digital Image Processing Using MATLAB, Prentice-Hall, 2004
%   $Revision: 1.10 $  $Date: 2004/12/15 20:15:38 $

d = [ ]; % Initialize d.
error(nargchk(3, 4, nargin)) % Verify correct no. of inputs.
n = size(CA, 1);             % Dimension of patterns.

% Protect against the possibility that the class number is
% included as an (n+1)th element of the vectors.
X = double(X(:, 1:n)); 
W = size(CA, 3); % Number of pattern classes.
K = size(X, 1);  % Number of patterns to classify.
if nargin == 3
   P(1:W) = 1/W; % Classes assumed equally likely.
else
   if sum(P) ~= 1 
      error('Elements of P must sum to 1.'); 
   end
end
% Compute the determinants.
for J = 1:W 
   DM(J) = det(CA(:, :, J)); 
end
    
% Evaluate the decision functions. Note the use of 
% function mahalanobis discussed in Section 12.2.
MA = MA'; % Organize the mean vectors as rows.
for J = 1:W
   C = CA(:,:,J);
   M = MA(J,:);
   k1 = log(P(J));
   k2 = 0.5*log(DM(J));
   L(1:K,1) = k1;
   DET(1:K,1) = k2;
   if P(J) == 0;
      D(1:K, J) = -inf;
   else
      D(:, J) = L - DET - 0.5*mahalanobis(X, C, M);
   end
end

% Find the coordinates of the maximum value in each row. These maxima
% maxima give the class of each pattern:
[i, j] = find(D==repmat(max(D')',1,size(D,2)));
% Re-use X. It now contains the max value along each column.
X = [i j]; 
% Eliminate multiple classifications of the same patterns. Since
% the class assignment when two or more decision functions give
% the same value is arbitrary, we need to keep only one.
X = sortrows(X);
[b, m] = unique(X(:,1));
X = X(m, :);
% X is now sorted, with the 2nd column giving the class of the
% pattern no. in the 1st col.;  i.e., X(j, 1) refers to the jth
% input pattern, and X(j, 2) is its the class number.

% Output the result of classification. d is a col. vector with
% length equal to the total no. of inout patterns. The elements of 
% d are the classes into which the patterns were classified.
d = X(:,2);