Why knnsearch () function slows down the code?
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I have to make feature vector in which I have to store distance between a candidate feature point and its four neighboring feature points. I am using knnsearch() for this purpose. However it slows down the code. How can I improve this?
Below is my code.
N = sum(cn_image(:) == 1) + sum(cn_image(:) == 3) + sum(cn_image(:) == 4); %Number of rows in feature Matrix
featr_vect = zeros(N,8);
for i = 1:size(cn_image,1)
for j = 1 : size(cn_image,2)
if (cn_image(i,j) == 1) || (cn_image(i,j) == 3) || (cn_image(i,j) == 4)
[Idx D] = knnsearch(cn_image(:), cn_image(i,j), 'k', 4, 'distance, 'euclidean');
end
end
end
2 commentaires
Jan
le 3 Avr 2017
Slows down the code compared to what? Of course searching for groups costs some time.
Réponses (1)
Jan
le 3 Avr 2017
Modifié(e) : Jan
le 3 Avr 2017
Currently your code overwrites Idx and D in each iteration. This is a massive waste of time. If only the last classification is wanted:
index = find(ismember(cn_image(:), [1, 3, 4]), 1, 'last');
[Idx D] = knnsearch(cn_image(:), cn_image(index), 'k', 4, 'distance, 'euclidean');
If the overwriting of Idx and D appears in the code posted here only and not in the real code: Please post the relevant part of the code. Such abbreviations are misleading frequently.
Optimizing code is hard, when the readers cannot run it. Better post some relevant input data, such that we can check our suggestions.
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