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hog_feature_vector.m
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hog_feature_vector.m
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function [feature] = hog_feature_vector(im)
% The given code finds the HOG feature vector for any given image. HOG
% feature vector/descriptor can then be used for detection of any
% particular object. The Matlab code provides the exact implementation of
% the formation of HOG feature vector as detailed in the paper "Pedestrian
% detection using HOG" by Dalal and Triggs
% INPUT => im (input image)
% OUTPUT => HOG feature vector for that particular image
% Convert RGB iamge to grayscale
if size(im,3)==3
im=rgb2gray(im);
end
im=double(im);
rows=size(im,1);
cols=size(im,2);
Ix=im; %Basic Matrix assignment
Iy=im; %Basic Matrix assignment
% Gradients in X and Y direction. Iy is the gradient in X direction and Iy
% is the gradient in Y direction
for i=1:rows-2
Iy(i,:)=(im(i,:)-im(i+2,:));
end
for i=1:cols-2
Ix(:,i)=(im(:,i)-im(:,i+2));
end
gauss=fspecial('gaussian',8); %% Initialized a gaussian filter with sigma=0.5 * block width.
angle=atand(Ix./Iy); % Matrix containing the angles of each edge gradient
angle=imadd(angle,90); %Angles in range (0,180)
magnitude=sqrt(Ix.^2 + Iy.^2);
% figure,imshow(uint8(angle));
% figure,imshow(uint8(magnitude));
% Remove redundant pixels in an image.
angle(isnan(angle))=0;
magnitude(isnan(magnitude))=0;
feature=[]; %initialized the feature vector
% Iterations for Blocks
for i = 0: rows/8 - 2
for j= 0: cols/8 -2
%disp([i,j])
mag_patch = magnitude(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
%mag_patch = imfilter(mag_patch,gauss);
ang_patch = angle(8*i+1 : 8*i+16 , 8*j+1 : 8*j+16);
block_feature=[];
%Iterations for cells in a block
for x= 0:1
for y= 0:1
angleA =ang_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
magA =mag_patch(8*x+1:8*x+8, 8*y+1:8*y+8);
histr =zeros(1,9);
%Iterations for pixels in one cell
for p=1:8
for q=1:8
%
alpha= angleA(p,q);
% Binning Process (Bi-Linear Interpolation)
if alpha>10 && alpha<=30
histr(1)=histr(1)+ magA(p,q)*(30-alpha)/20;
histr(2)=histr(2)+ magA(p,q)*(alpha-10)/20;
elseif alpha>30 && alpha<=50
histr(2)=histr(2)+ magA(p,q)*(50-alpha)/20;
histr(3)=histr(3)+ magA(p,q)*(alpha-30)/20;
elseif alpha>50 && alpha<=70
histr(3)=histr(3)+ magA(p,q)*(70-alpha)/20;
histr(4)=histr(4)+ magA(p,q)*(alpha-50)/20;
elseif alpha>70 && alpha<=90
histr(4)=histr(4)+ magA(p,q)*(90-alpha)/20;
histr(5)=histr(5)+ magA(p,q)*(alpha-70)/20;
elseif alpha>90 && alpha<=110
histr(5)=histr(5)+ magA(p,q)*(110-alpha)/20;
histr(6)=histr(6)+ magA(p,q)*(alpha-90)/20;
elseif alpha>110 && alpha<=130
histr(6)=histr(6)+ magA(p,q)*(130-alpha)/20;
histr(7)=histr(7)+ magA(p,q)*(alpha-110)/20;
elseif alpha>130 && alpha<=150
histr(7)=histr(7)+ magA(p,q)*(150-alpha)/20;
histr(8)=histr(8)+ magA(p,q)*(alpha-130)/20;
elseif alpha>150 && alpha<=170
histr(8)=histr(8)+ magA(p,q)*(170-alpha)/20;
histr(9)=histr(9)+ magA(p,q)*(alpha-150)/20;
elseif alpha>=0 && alpha<=10
histr(1)=histr(1)+ magA(p,q)*(alpha+10)/20;
histr(9)=histr(9)+ magA(p,q)*(10-alpha)/20;
elseif alpha>170 && alpha<=180
histr(9)=histr(9)+ magA(p,q)*(190-alpha)/20;
histr(1)=histr(1)+ magA(p,q)*(alpha-170)/20;
end
end
end
block_feature=[block_feature histr]; % Concatenation of Four histograms to form one block feature
end
end
% Normalize the values in the block using L1-Norm
block_feature=block_feature/sqrt(norm(block_feature)^2+.01);
feature=[feature block_feature]; %Features concatenation
end
end
feature(isnan(feature))=0; %Removing Infinitiy values
% Normalization of the feature vector using L2-Norm
feature=feature/sqrt(norm(feature)^2+.001);
for z=1:length(feature)
if feature(z)>0.2
feature(z)=0.2;
end
end
feature=feature/sqrt(norm(feature)^2+.001);
% toc;