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| %A MATLAB Toolbox
%
%Compare two segmentation results using the Boundary Displacement Error
%
%IMPORTANT: The input two images must have the same size!
%
%Authors: John Wright, and Allen Y. Yang
%Contact: Allen Y. Yang <yang@eecs.berkeley.edu>
%
%(c) Copyright. University of California, Berkeley. 2007.
%
%Notice: The packages should NOT be used for any commercial purposes
%without direct consent of their author(s). The authors are not responsible
%for any potential property loss or damage caused directly or indirectly by the usage of the software.
function [averageError, returnStatus] = compare_image_boundary_error(imageLabels1, imageLabels2);
returnStatus = 0;
[imageX, imageY] = size(imageLabels1);
if imageX~=size(imageLabels2,1) | imageY~=size(imageLabels2,2)
error('The sizes of the two comparing images must be the same.');
end
if isempty(find(imageLabels1~=imageLabels1(1)))
% imageLabels1 only has one group
boundary1 = zeros(size(imageLabels1));
boundary1(1,:) = 1;
boundary1(:,1) = 1;
boundary1(end,:) = 1;
boundary1(:,end) = 1;
else
% Generate boundary maps
[cx,cy] = gradient(imageLabels1);
[boundaryPixelX{1},boundaryPixelY{1}] = find((abs(cx)+abs(cy))~=0);
boundary1 = abs(cx) + abs(cy) > 0;
end
if isempty(find(imageLabels2~=imageLabels2(1)))
% imageLabels2 only has one group
boundary2 = zeros(size(imageLabels2));
boundary2(1,:) = 1;
boundary2(:,1) = 1;
boundary2(end,:) = 1;
boundary2(:,end) = 1;
else
% Generate boundary maps
[cx,cy] = gradient(imageLabels2);
[boundaryPixelX{2},boundaryPixelY{2}] = find((abs(cx)+abs(cy))~=0);
boundary2 = abs(cx) + abs(cy) > 0;
end
% boundary1 and boundary2 are now binary boundary masks. compute their
% distance transforms:
D1 = bwdist(boundary1);
D2 = bwdist(boundary2);
% compute the distance of the pixels in boundary1 to the nearest pixel in
% boundary2:
dist_12 = sum(sum(boundary1 .* D2 ));
dist_21 = sum(sum(boundary2 .* D1 ));
avgError_12 = dist_12 / sum(sum(boundary1));
avgError_21 = dist_21 / sum(sum(boundary2));
averageError = (avgError_12 + avgError_21) / 2;[/B][/I]
[B][I]
%A MATLAB Toolbox
%
%Compare two segmentation results using
%1. Probabilistic Rand Index
%2. Variation of Information
%3. Global Consistency Error
%
%IMPORTANT: The two input images must have the same size!
%
%Authors: John Wright, and Allen Y. Yang
%Contact: Allen Y. Yang <yang@eecs.berkeley.edu>
%
%(c) Copyright. University of California, Berkeley. 2007.
%
%Notice: The packages should NOT be used for any commercial purposes
%without direct consent of their author(s). The authors are not responsible
%for any potential property loss or damage caused directly or indirectly by the usage of the software.
function [ri,gce,vi]=compare_segmentations(sampleLabels1,sampleLabels2)
% compare_segmentations
%
% Computes several simple segmentation benchmarks. Written for use with
% images, but works for generic segmentation as well (i.e. if the
% sampleLabels inputs are just lists of labels, rather than rectangular
% arrays).
%
% The measures:
% Rand Index
% Global Consistency Error
% Variation of Information
%
% The Rand Index can be easily extended to the Probabilistic Rand Index
% by averaging the result across all human segmentations of a given
% image:
% PRI = 1/K sum_1^K RI( seg, humanSeg_K ).
% With a little more work, this can also be extended to the Normalized
% PRI.
%
% Inputs:
% sampleLabels1 - n x m array whose entries are integers between 1
% and K1
% sampleLabels2 - n x m (sample size as sampleLabels1) array whose
% entries are integers between 1 and K2 (not
% necessarily the same as K1).
% Outputs:
% ri - Rand Index
% gce - Global Consistency Error
% vi - Variation of Information
%
% NOTE:
% There are a few formulas here that look less-straightforward (e.g.
% the log2_quotient function). These exist to handle corner cases
% where some of the groups are empty, and quantities like 0 *
% log(0/0) arise...
%
% Oct. 2006
% Questions? John Wright - <a href="mailto:jnwright@uiuc.edu">jnwright@uiuc.edu</a>
[imWidth,imHeight]=size(sampleLabels1);
[imWidth2,imHeight2]=size(sampleLabels2);
N=imWidth*imHeight;
if (imWidth~=imWidth2)||(imHeight~=imHeight2)
disp( 'Input sizes: ' );
disp( size(sampleLabels1) );
disp( size(sampleLabels2) );
error('Input sizes do not match in compare_segmentations.m');
end;
% make the group indices start at 1
if min(min(sampleLabels1)) < 1
sampleLabels1 = sampleLabels1 - min(min(sampleLabels1)) + 1;
end
if min(min(sampleLabels2)) < 1
sampleLabels2 = sampleLabels2 - min(min(sampleLabels2)) + 1;
end
segmentcount1=max(max(sampleLabels1));
segmentcount2=max(max(sampleLabels2));
% compute the count matrix
% from this we can quickly compute rand index, GCE, VOI, ect...
n=zeros(segmentcount1,segmentcount2);
for i=1:imWidth
for j=1:imHeight
u=sampleLabels1(i,j);
v=sampleLabels2(i,j);
n(u,v)=n(u,v)+1;
end;
end;
ri = rand_index(n);
gce = global_consistancy_error(n);
vi = variation_of_information(n);
return;
% the performance measures
% the rand index, in [0,1] ... higher => better
% fast computation is based on equation (2.2) of Rand's paper.
function ri = rand_index(n)
N = sum(sum(n));
n_u=sum(n,2);
n_v=sum(n,1);
N_choose_2=N*(N-1)/2;
ri = 1 - ( sum(n_u .* n_u)/2 + sum(n_v .* n_v)/2 - sum(sum(n.*n)) )/N_choose_2;
% global consistancy error (from BSDS ICCV 01 paper) ... lower => better
function gce = global_consistancy_error(n)
N = sum(sum(n));
marginal_1 = sum(n,2);
marginal_2 = sum(n,1);
% the hackery is to protect against cases where some of the marginals are
% zero (should never happen, but seems to...)
E1 = 1 - sum( sum(n.*n,2) ./ (marginal_1 + (marginal_1 == 0)) ) / N;
E2 = 1 - sum( sum(n.*n,1) ./ (marginal_2 + (marginal_2 == 0)) ) / N;
gce = min( E1, E2 );
% variation of information a "distance", in (0,vi_max] ... lower => better
function vi = variation_of_information(n)
N = sum(sum(n));
joint = n / N; % the joint pmf of the two labels
marginal_2 = sum(joint,1); % row vector
marginal_1 = sum(joint,2); % column vector
H1 = - sum( marginal_1 .* log2(marginal_1 + (marginal_1 == 0) ) ); % entropy of the first label
H2 = - sum( marginal_2 .* log2(marginal_2 + (marginal_2 == 0) ) ); % entropy of the second label
MI = sum(sum( joint .* log2_quotient( joint, marginal_1*marginal_2 ) )); % mutual information
vi = H1 + H2 - 2 * MI;
% log2_quotient
% helper function for computing the mutual information
% returns a matrix whose ij entry is
% log2( a_ij / b_ij ) if a_ij, b_ij > 0
% 0 if a_ij is 0
% log2( a_ij + 1 ) if a_ij > 0 but b_ij = 0 (this behavior should
% not be encountered in practice!
function lq = log2_quotient( A, B )
lq = log2( (A + ((A==0).*B) + (B==0)) ./ (B + (B==0)) ); |
Evaluation de la performance des méthodes de segmentation
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