find the most similar vector from 10 vectors

Question

talayeh ghodsi 2019-6-30

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/469571-find-the-most-similar-vector-from-10-vectors

编辑： Stephen23 2019-6-30

untitled.jpg

hello every one.

I have one echo image and 10 ct images. i have extract Local binary pattern of them, and i have calculated the squared error between lbp of echo and lbp of each of the 10 ct images. now could anyone answer me how can i find the most similar ct image to echo image from 10 squared errors?

ct124=rgb2gray(imread('4-chamber view124_croped.jpg'));

ct125=rgb2gray(imread('4-chamber view125_croped.jpg'));

ct126=rgb2gray(imread('4-chamber view126_croped.jpg'));

ct127=rgb2gray(imread('4-chamber view127_croped.jpg'));

ct128=rgb2gray(imread('4-chamber view128_croped.jpg'));

ct129=rgb2gray(imread('4-chamber view129_croped.jpg'));

ct130=rgb2gray(imread('4-chamber view130_croped.jpg'));

ct131=rgb2gray(imread('4-chamber view131_croped.jpg'));

ct132=rgb2gray(imread('4-chamber view132_croped.jpg'));

ct133=rgb2gray(imread('4-chamber view133_croped.jpg'));

ct134=rgb2gray(imread('4-chamber view134_croped.jpg'));

ct135=rgb2gray(imread('4-chamber view135_croped.jpg'));

ct136=rgb2gray(imread('4-chamber view136_croped.jpg'));

ct137=rgb2gray(imread('4-chamber view137_croped.jpg'));

ct138=rgb2gray(imread('4-chamber view138_croped.jpg'));

echo=rgb2gray(imread('echo.jpg'));

lbpct124 = extractLBPFeatures(ct124,'Upright',false);

lbpct125 = extractLBPFeatures(ct125,'Upright',false);

lbpct126 = extractLBPFeatures(ct126,'Upright',false);

lbpct127 = extractLBPFeatures(ct127,'Upright',false);

lbpct128 = extractLBPFeatures(ct128,'Upright',false);

lbpct129 = extractLBPFeatures(ct129,'Upright',false);

lbpct130 = extractLBPFeatures(ct130,'Upright',false);

lbpct131 = extractLBPFeatures(ct131,'Upright',false);

lbpct132 = extractLBPFeatures(ct132,'Upright',false);

lbpct133 = extractLBPFeatures(ct133,'Upright',false);

lbpct134 = extractLBPFeatures(ct134,'Upright',false);

lbpct135 = extractLBPFeatures(ct135,'Upright',false);

lbpct136 = extractLBPFeatures(ct136,'Upright',false);

lbpct137 = extractLBPFeatures(ct137,'Upright',false);

lbpct138 = extractLBPFeatures(ct138,'Upright',false);

lbpecho = extractLBPFeatures(echo,'Upright',false);

echoVsct124 = (lbpecho - lbpct124).^2;

echoVsct125 = (lbpecho - lbpct125).^2;

echoVsct126 = (lbpecho - lbpct126).^2;

echoVsct127 = (lbpecho - lbpct127).^2;

echoVsct128 = (lbpecho - lbpct128).^2;

echoVsct129 = (lbpecho - lbpct129).^2;

echoVsct130 = (lbpecho - lbpct130).^2;

echoVsct131 = (lbpecho - lbpct131).^2;

echoVsct132 = (lbpecho - lbpct132).^2;

echoVsct133 = (lbpecho - lbpct133).^2;

echoVsct134 = (lbpecho - lbpct134).^2;

echoVsct135 = (lbpecho - lbpct135).^2;

echoVsct136 = (lbpecho - lbpct136).^2;

echoVsct137 = (lbpecho - lbpct137).^2;

echoVsct138 = (lbpecho - lbpct138).^2;

figure

bar([echoVsct124; echoVsct125; echoVsct126; echoVsct127; echoVsct128; echoVsct129; echoVsct130; echoVsct131; echoVsct132; echoVsct133; echoVsct134; echoVsct135; echoVsct136; echoVsct137; echoVsct138]','grouped')

title('Squared Error of LBP Histograms')

xlabel('LBP Histogram Bins')

legend('echo vs ct124','echo vs ct125','echo vs ct126','echo vs ct127','echo vs ct128','echo vs ct129','echo vs ct130','echo vs ct131','echo vs ct132','echo vs ct133','echo vs ct134','echo vs ct135','echo vs ct136','echo vs ct137','echo vs ct138')