%IM_DBR Image Database Retrieval GUI
%
% [RANK,TARG,OUTL] = IM_DBR(DBASE,FSETS,CLASSF,COMB)
%
% INPUT
% DBASE - Dataset or datafile with N object images
% FSETS - Cell array with maximum 4 feature sets
% CLASSF - Cell array with untrained classifiers (Default: KNNC([],1))
% COMB - Combining classifier (Default: MEANC)
%
% OUTPUT
% RANK - Index array ranking the N object images
% TARG - Index array pointing to user defined target images
% OUTL - Index array pointing to user defined outlier images
%
% DESCRIPTION
% This command generates a Graphical User Interface (GUI) enabling the user
% to label a database of images in 'target' and 'outlier' images in an
% interactive and iterative way. Up to four feature sets can be given and
% corresponding classifiers that assist the user by predict an object ranking
% based on classification confidences for the 'target' class.
%
% The GUI shows the top-10 of the ranking and the user should classify
% them as targets or outliers (original object labels in DBASE are
% neglected). There are buttons for browsing through the ranked database
% or through the selected targets and outliers. Classifiers can be trained
% according to two different strategies using the top right buttons:
% Classify - uses all stored target and outlier objects (shown in the top
% left windows) for building a training set as well as the
% hand labeled images in the present screen.
% Label - uses just the hand labeled images in the present screen
% and neglects the stored targets and outliers. This enables
% a more flexible, but still controlled browsing throug the
% database.
% Reset - Resets the entire procedure by deleting all selected targets
% and outliers.
% Quit - Deletes the GUI and returns the ranking and selected targets
% and outliers to the user.
% A few additional buttons and sliders for controlling the system behavior:
% - Delete and move buttons for the selected targets and outliers
% - Weights for the feature sets. For each feature set a different
% classifier is computed generating target confidences for all images.
% This influences the operation of the combiniong classifier.
% The weights can be changed by a slider for every feature set.
% By default weights are 1.
% - Two buttons for setting all labels as target ('All target') or outlier
% ('All outlier').
% - Labels for the individual images can be changed by a mouse-click in the
% image or on the image check-box.
% - For all images a target confidence is computed. Depending on the 'all'
% and 'unlabeled' radio buttons at the bottom the ranking of all images
% or of the yet unlabeled images are shown.
% Note: It is not an error, but for most classifiers useless or
% counterproductive to label an object as target as well as outlier.
%
% EXAMPLE
% % This example assumes that the Kimia images are available as datafile
% % and that the DipImage image processing package is available.
% prwaitbar on
% a = kimia_images;
% x = im_moments(a,'hu');
% x = setname(x,'Hu moments');
% y = im_measure(a,a,{'size','perimeter','ccbendingenergy'});
% y = setname(y,'Shape features');
% [R,T,L] = im_dbr(a,{x,y}); % do your own search
% delfigs
% figure(1); show(a(R,:)); % show ranking
% figure(2); show(a(T,:)); % show targets
% figure(3); show(a(L,:)); % show outliers
% showfigs
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, DATAFILES, MAPPINGS, KNNC, MEANC
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function [R,T,L] = im_dbr(dbase,featsets,classf,comb);
if sscanf(version('-release'),'%i') < 14
error('IM_DBR needs Matlab version 14 or higher')
end
if nargin < 4, comb = meanc; end
if nargin < 3, classf = knnc([],1); end
[R,T,L] = image_dbr(dbase,featsets,classf,comb);
return