Vfrdtyky

I have a large binary matrix. I want to reduce the size of this matrix by using knn-approximation. What my idea is to cluster the matrix in groups of 4 neighbors and replace the group with a 1, if the number of 1s in the group is greater than or equal to the number of zeros.

To be concrete, let the matrix be

1 0 0 1 0 

0 1 1 0 0 

1 1 0 0 0 

0 1 1 1 0

0 0 1 1 0

1 0 0 1 0

First I want to create neighborhood group as

1 0 |0 1| 0| 

0 1 |1 0| 0|

------------

1 1 |0 0| 0| 

0 1 |1 1| 0|

------------

0 0 |1 1| 0|

------------

and then the final matrix I want to generate is

1 1 0

1 1 0

0 1 0

by replacing the group with the majority score. How can I efficiently do this is MATLAB?

Initially I tried getting it to work using imresize but couldn't quite get it without "hacks" (it was off by 1 value in all my "proper" attempts).

imresize(M, ceil(size(M)/2), 'bilinear') >= 0.4 % This works but is hacky and not recommended!

However, I can think of a way to solve this using 2D convolution. Note that I pad the array (which requires a toolbox) in order to simplify the indexing stage at the end:

function C = q53247013(M)



if nargin < 1



  M = [

    1 0 0 1 0 

    0 1 1 0 0 

    1 1 0 0 0 

    0 1 1 1 0

    0 0 1 1 0

    1 0 0 1 0];



end



% Constants:

BLK_SZ = 2;

A = ones(BLK_SZ);



% Pad array if needed (note: this WILL require modification if BLK_SZ > 2 ):    

padBottom = rem(size(M,BLK_SZ),1);

padRight = rem(size(M,BLK_SZ),2);

M = padarray(M, [padBottom, padRight], 'replicate', 'post');



% Perform convolution:    

C = conv2(M, A, 'valid') >= ceil(BLK_SZ^2 / 2);



% Remove every other row and column:

C = C(1:2:end, 1:2:end);

Another alternative is the blockproc function:

C = blockproc(M, [2 2], @(x)sum(x.data(:))) >= 2;