wavefeat_all.m

基于小波变换的特征检索算法

function [fm, fmm, fml, we] = wavefeat_all(dbname, dirname, ...
					   nlevels, filt, edge)
% WAVEFEAT_ALL Wavelet-based feature extraction from ALL images
%
% Input:
%	dbname:	name of the test collection, e.g. 'VisTex'
%       dirname: directory that contains all images, e.g. '../VisTex/sub128'
%	nlevels: (optional) number of wavelet pyramid levels (default 3)
%	filt:	(optional) string naming a standard filter 
%		(see wfilters, default 'haar')
%	edge:	(optional) specifies edge-handling 
%		(see dwtmode, default 'per')
%
% Output: (one column per image)	
%	fm:	feature vectors based on mean and standard deviation
%		of the magnitute of the wavelet coefficients
%	fmm: 	feature vectors based on generalized Gaussian pdf parameters
%		(Moment Matching Estimation)
%	fml: 	feature vectors based on generalized Gaussian pdf parameters
%		(Maximum Likelihood Estimation)
%	we:	entropy of wavelet subbands
%
% See also: WAVEFEAT

% Suppose that dbname is a function that returns the names of texture classes
name = eval(dbname);%执行
ntexs = length(name);

nsubs = 16;

% Checking other inputs,使得'nlevels','filt','per'不是变量而是定义为指定的默认值
if ~exist('nlevels', 'var')
    nlevels = 3;
end

if ~exist('filt', 'var')
    filt = 'haar';
end

if ~exist('edge', 'var')
    edge = 'per';
end

% Filters from name
[LoF_D, HiF_D] = wfilters(filt, 'd');%小波分解harr过滤

% Set edge-handling mode
% dwtmode(edge);
st = dwtmode('status', 'nodisp');%returns in st the current mode and no text (status or warning) is displayed in the MATLAB command window
if ~strcmp(st, edge)
    dwtmode(edge); % DWT扩展模式为edge
end

% Initialize
fm  = [];
fmm = [];
fml = [];
we  = [];

for p = 1:ntexs
    for q = 1:nsubs
        file = sprintf('%s/%s.%02d.tif', dirname, name{p}, q);
  
	[fv1, fv2, fv3, w] = wavefeat(file, nlevels, LoF_D, HiF_D);
	
	fm  = [fm,  fv1];
	fmm = [fmm, fv2];
	fml = [fml, fv3];
	we  = [we, w];	
    end
end