Image Classification Using MATLAB SOM/LVQ

I like to think of myself as a hacker :-), not in today’s sense of the word [person who breaks into secured computer areas] but as a hacker in the sense of first two definitions found here. I like to experiment with things especially related to computers and Artificial Intelligence in particular. MATLAB happens to be a virtual toolbox for the trade pun intended :-), using some of its toolboxes we will see how we can solve a ubiquitous problem that faces most persons with a nice camera and a voracious appetite for taking pictures.

Now, I don’t have an expensive Nikon, but I do have loads of pictures; and one day i was trying to find this particular picture when it occurred to me that if i could not remember the name or date when i took the picture it would require me to search blindly every picture I had in order to find that special one. Now what if i had some way of finding the picture based on what i could remember of it? ie. environment in which it was taken, colours and objects along with some other visual specifications, wouldn’t that be cool.

So I went for the proverbial toolbox MATLAB, which tools will I need?

Neural Network -> selforgmap, lvqnet, vec2ind, ind2vec
Image Processing -> imhist, imresize, rgb2gray, imread

Other:

mspaint

Note: For this demonstration I will be using:

MATLAB R2011b on Windows 7
Pictures found at C:\Users\Public\Pictures\Sample Pictures

Ok lets do it, start up MATLAB, copy and paste all the pics from the above directory to MATLAB’s current directory. {Chrysanthemum, Desert, Hydrangeas, Jellyfish, Koala, Lighthouse, Penguins, Tulips}.

In the new edit window copy and paste the code as given below. Save file as scan.m

function scan(img)

files = dir('*.jpg');

hist = [];

for n = 1 : length(files)

filename = files(n).name;

file = imread(filename);

hist = [hist, imhist(rgb2gray(imresize(file,[ 50 50])))]; %#ok

end

som = selforgmap([10 10]);

som = train(som, hist);

t = som(hist); %extract class data

net = lvqnet(10);

net = train(net, hist, t);

like(img, hist, files, net)

end

Links to the functions used were provided above therefore i will not be going into the details of how they work, however there is a narrative with regard to the workings of the code:

The code starts by searching the current MATLAB directory for all files with a .jpg extension. On each iteration of the loop an image is loaded and resized to 50 x 50, it is then converted to greyscale and a histogram measurement is taken of its pixels [feature vector]; the results are then appended to a 256 x n matrix with n been the number of images scanned.

A self organizing map network is then used to identify classes into which the images fall. The feature matrix and class data is used to train a Learning Vector Quantization neural network, that will be used for classification of images presented to it.

Next we will create a function to display all matching images for an image we submit to the LVQ network.

function like(im, hist, files , net)

hs = imhist(rgb2gray(imresize(im,[50 50])));

cls = vec2ind(net(hs));

[~, n] = size(hist);

for i = 1 : n

if(cls == vec2ind(net(hist(:, i))))

figure('name', files(i).name);

imshow(imresize(imread(files(i).name), [100 100]))

end

Download a picture of a koala and save it outside your MATLAB path as koalatest.jpg
At the MATLAB command prompt type scan(imread(‘[replace with path to koalatest]\koalatest.jpg’)
After a minute or two the networks should have been trained and a figure displaying the matching koala.jpg image shown to you.

NOTE: As explained above this is hacking, not production code I wrote this up in about 20 minutes as a demonstration for classification of images, with imagination this could be extended to classify things like sound for example using a feature map crated from humming a tune to find a song with a similar melody.

LVQ Been trained: