How to create feature vector ?

Someone suggested me to do as follows:

"take the histogram and construct a feature vector that has several comparison metrics, such as mean, std dev, skewness, range, RMS difference, and median absolute difference. Then compare feature vectors to see which are closest "

I have tried to do so which is mentioned below.

clear all; close all; clc;

im1=imread('106a.jpg');
im2=imread('107a.jpg');

im1g=rgb2gray(im1); 
im2g=rgb2gray(im2);

%%Calculating mean of im1
grayImage=rgb2gray(im1);

subplot(1, 2, 1);
imshow(grayImage, []);
title('Original Grayscale Image');
set(gcf, 'Position', get(0,'Screensize')); % Enlarge figure to full
%screen.

% Let's get its histogram.
[pixelCount grayLevels] = imhist(grayImage);
subplot(1, 2, 2);
bar(pixelCount);
title('Histogram of original image');
xlim([0 grayLevels(end)]); % Scale x axis manually.
yRange = ylim;
% Calculate the mean gray level
meanGL = sum(pixelCount .* grayLevels) / sum(pixelCount)

%%Calculating mean of  im2

grayImage2=rgb2gray(im2);

figure, subplot(1, 2, 1);
imshow(grayImage2, []);
title('Original Grayscale Image');
set(gcf, 'Position', get(0,'Screensize')); % Enlarge figure to full
%screen.

% Let's get its histogram.
[pixelCount2 grayLevels2] = imhist(grayImage2);
subplot(1, 2, 2);
bar(pixelCount2);
title('Histogram of original image');
xlim([0 grayLevels2(end)]); % Scale x axis manually.
yRange2 = ylim;
% Calculate the mean gray level
meanGL2 = sum(pixelCount2 .* grayLevels2) / sum(pixelCount2)

%%Calculating standard deviation

st_d1=std(double(im1));
st_d2=std(double(im2));

%%Calculating Skewness

sk1=skewness(double(im1));
sk2=skewness(double(im2));

%%Calculating RMS

rms1=rms(im1);
rms2=rms(im2);

%%Calculating  median absolute 

md1=mad(double(im1));
md2=mad(double(im2));

%%Contruct a feature vector

fv1=[ meanGL,  st_d1, sk1, rms1, md1 ] 
fv2= [ meanGL2, st_d2, sk2, rms2, md2 ]

But it seems like I have not created feature vectors correctly because of reasons:

1. dimensions of meanGL, st_d1, sk1, rms1 and md1 are different. 2. after evaluating above code in MATLAB editor, I am getting error

" Error using horzcat
Dimensions of matrices being concatenated are not consistent.

Error in oct6 (line 71)
fv1=[ meanGL,  st_d1, sk1, rms1, md1 ] ;   "

But it

[Walter Roberson]

http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup

Also, When you put a breakpoint in at the line and run the program, what is size() of each of those variables ?

[Image Analyst]

Complete duplicate of http://www.mathworks.com/matlabcentral/answers/89252#comment_172673

Answer 1

[saranya]

As u have converted the image to gray scale it gives the 1D matrix.. so u get the "hor concate" error... try changing this code for the part of calculating mean and try u will the result..

%%Calculating mean of im1 
 grayImage=rgb2gray(im1);
 meanGL = mean(im1);

now u will get the feature vector... for clarification see

size(meanGL)
size(st_d1)
size(sk1)
size(rms1)
size(md1)

see this value for ur previous coding and this coding..