MATLAB Answers

4

how PCA can be applied to an image to reduce its dimensionality with example?

Asked by G Prasanth Reddy on 24 Dec 2014
Latest activity Commented on by Image Analyst
on 25 Jul 2019
This question was flagged by Walter Roberson
Dimensionality reduction

  1 Comment

i run your code but it is not work with me this error in the picture in the file plz see it and tell me what is the wrong

Sign in to comment.

9 Answers

Answer by Image Analyst
on 24 Dec 2014
 Accepted Answer

Here's code I got from Spandan, one of the developers of the Image Processing Toolbox at the Mathworks:
Here some quick code for getting principal components of a color image. This code uses the pca() function from the Statistics Toolbox which makes the code simpler.
I = double(imread('peppers.png'));
X = reshape(I,size(I,1)*size(I,2),3);
coeff = pca(X);
Itransformed = X*coeff;
Ipc1 = reshape(Itransformed(:,1),size(I,1),size(I,2));
Ipc2 = reshape(Itransformed(:,2),size(I,1),size(I,2));
Ipc3 = reshape(Itransformed(:,3),size(I,1),size(I,2));
figure, imshow(Ipc1,[]);
figure, imshow(Ipc2,[]);
figure, imshow(Ipc3,[]);
In case you don’t want to use pca(), the same computation can be done without the use of pca() with a few more steps using base MATLAB functions.
Hope this helps.
-Spandan

  5 Comments

They are the principal components of the color image. It's like you rotated the R, G, and B axes so that it goes down through the major axis of the 3D color gamut.
pranav
2016 年 4 月 2 日
How can i create a new file with selected bands if my dimensions are 200x150x50 and i have selected few bands 1,3,5,11,24 and now want to change it to 200x150x5??
Use cat(3, ....)
newImage = cat(3, data(:,:,1), data(:,:,3), data(:,:,5), data(:,:,11), data(:,:,24));

Sign in to comment.


Answer by Devan Marçal on 13 Aug 2015

Hi,
in your example you used PCA in just one image. I have an image bank a total of ~ 800 images. If I make a loop (if, while, etc ..) using the PCA function for each image individually, will be using this command wrong or inefficiently?
Thanks a lot.
Devan

  8 Comments

Hello, it is weird but, in line 114 it gives inner dimension error.
transformedImagePixelList = listOfRGBValues * coeff;
For 'coloredChips' image example, size(listOfRGBValues)=202538x3
size(coeff)=202538x2
Why do you think is happening ?
Darshan Jain 2019 年 7 月 25 日
Hey @ImageAnalyst,
I checked out your script, I had a small question, How could I plot the colored image back in three plots (showing approximation by pca1, then pca1 and pca2 and then followed by pca1, pca2 and pca3).
I tried doing using the imfuse comand "imfuse(pca1,pca2)", the clarity improved well, but i'm not able to reproduce the same colors. (see the attached image)
I think this is because I need to normalize the data, and then un-normalize it back before plotting. (I'm not sure though)
Etworld, I just ran the colored chips image and it ran fine. Did you change my code at all?
00_Screenshot.png
Darshan: where did your colors come from? I don't understand what your "approximations" are supposed to be. But anyway, you can stitch images side by side if they are all RGB images to begin with:
wideImage = [rgbImage1, rgbImage2, rgbImage3];

Sign in to comment.


Answer by Ram
on 9 Nov 2015

how to create additional columns in a image

  1 Comment

This is not related at all. Please start a new question.

Sign in to comment.


Answer by Shaveta Arora on 30 Jan 2016

Can I have the pca code used in this color image example

  6 Comments

Image Analyst
2016 年 1 月 31 日
You must not have the Statistics and Machine Learning Toolbox.
Might possible. Pls share this pca function to save in my folder.
Image Analyst
2016 年 1 月 31 日
I can't. It would not be legal. You either have to buy the toolbox from the Mathworks, or implement it yourself from low level code.

Sign in to comment.


Answer by Anitha Anbazhagan on 17 Sep 2016

I have 200 ROIs from each of the 50 images. For each ROI, I have 96 feature vectors for four different frequency bands. It seems very high dimensional. How to apply PCA for this? PCA should be applied to data matrix. Do I have to apply for each image or each ROI?

  1 Comment

It depends on if you want PCA components on each image individually, or the PCA components of the group as a whole.

Sign in to comment.


Answer by Mina Kh
on 11 Dec 2016

Hi. I have multispectral( multi channel) data and I want to apply PCA to reduce the number of channel. Can u give me some hint?Which code i have to use?

  0 Comments

Sign in to comment.


Answer by Arathy Das on 20 Dec 2016

How can i extract three texture features among the 22 using PCA?

  1 Comment

I think you should start your own discussion with your own data or images. If you have 22 PCA columns, then just extract the 3 you want as usual.
pca3 = pca22(:, 1:3); % or whatever.

Sign in to comment.


Answer by joynjo
on 24 Mar 2018

How to visualize the result of PCA image in pseudocolor?

  1 Comment

imshow(PC1); % Display the first principal component image.
colormap(jet(256));

Sign in to comment.


Answer by F M Anim Hossain on 6 Apr 2018

I'm new to the concept of PCA. I'm trying to develop something that can recognize color features from different images. Is it possible to do it with the help of PCA?

  0 Comments

Sign in to comment.