image_phase_feature_g4_h4.m

局部滤波器的特征提取方法

function [image_phase_vector] = image_phase_feature_G4_H4(image, scale_1_sample, scale_2_sample, scale_3_sample)
% written by xiaoxun zhang. 
% 2004-08-22

% image = double(imread('001.jpg'));

image_phase_matrix = compute_image_phase_feature_G4_H4(image);
image_phase_vector_real = [];
image_phase_vector_imaginary = [];
for orientation_index = 1:size(image_phase_matrix, 2)
    phase_matrix = image_phase_matrix(orientation_index).real_matrix;
    phase_vector = mean_sample(phase_matrix, scale_1_sample, scale_1_sample);
    phase_vector = norm_0mean_1variance(phase_vector);
    image_phase_vector_real = [image_phase_vector_real; phase_vector];
    phase_matrix = image_phase_matrix(orientation_index).imaginary_matrix;
    phase_vector = mean_sample(phase_matrix, scale_1_sample, scale_1_sample);
    phase_vector = norm_0mean_1variance(phase_vector);
    image_phase_vector_imaginary = [image_phase_vector_imaginary; phase_vector];
end

% image_gaussian_filter_width13 = quadrature_filter(image, gaussian_filter_width13, gaussian_filter_width13');
image_2 = imresize(image, 1/2, 'bilinear');
image_phase_matrix = compute_image_phase_feature_G4_H4(image_2);
image_phase_vector_real_2 = [];
image_phase_vector_imaginary_2 = [];
for orientation_index = 1:size(image_phase_matrix, 2)
    phase_matrix = image_phase_matrix(orientation_index).real_matrix;
    phase_vector = mean_sample(phase_matrix, scale_2_sample, scale_2_sample);
    phase_vector = norm_0mean_1variance(phase_vector);
    image_phase_vector_real_2 = [image_phase_vector_real_2; phase_vector];
    phase_matrix = image_phase_matrix(orientation_index).imaginary_matrix;
    phase_vector = mean_sample(phase_matrix, scale_2_sample, scale_2_sample);
    phase_vector = norm_0mean_1variance(phase_vector);    
    image_phase_vector_imaginary_2 = [image_phase_vector_imaginary_2; phase_vector];
end

% image_gaussian_filter_width13 = quadrature_filter(image_2, gaussian_filter_width13, gaussian_filter_width13');
image_4 = imresize(image, 1/4, 'bilinear');
image_phase_matrix = compute_image_phase_feature_G4_H4(image_4);
image_phase_vector_real_4 = [];
image_phase_vector_imaginary_4 = [];
for orientation_index = 1:size(image_phase_matrix, 2)
    phase_matrix = image_phase_matrix(orientation_index).real_matrix;
    phase_vector = mean_sample(phase_matrix, scale_3_sample, scale_3_sample);
    phase_vector = norm_0mean_1variance(phase_vector);    
    image_phase_vector_real_4 = [image_phase_vector_real_4; phase_vector];
    phase_matrix = image_phase_matrix(orientation_index).imaginary_matrix;
    phase_vector = mean_sample(phase_matrix, scale_3_sample, scale_3_sample);
    phase_vector = norm_0mean_1variance(phase_vector);    
    image_phase_vector_imaginary_4 = [image_phase_vector_imaginary_4; phase_vector];
end

image_phase_vector_g4_h4_real = [image_phase_vector_real; image_phase_vector_real_2; image_phase_vector_real_4];
image_phase_vector_g4_h4_imaginary = [image_phase_vector_imaginary; image_phase_vector_imaginary_2; image_phase_vector_imaginary_4];

% for G4/H4
image_phase_vector = [image_phase_vector_g4_h4_real; image_phase_vector_g4_h4_imaginary];   
% return;