How to create a features vector after extracting them from an Image?

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Elias Unk
Elias Unk 2017-4-29
评论： Image Analyst 2017-4-30
I extracted color features like mean,standard deviation,variance etc...note that i extracted from different color spaces like RGB,HSV,YCbCr and extracted the same features for each color plane,R,G,B,H,...etc.. and texture features like correlation,contrast,energy etc..note that i used GLCM to extract my texture features and i did that for 4 orientations so some of my texture features is a 1D 4 elements vector. How can i create 1 final vector that summarizes everything for an image and which way/ways could be used so i can actually find out what features matter most so i can increase their weights accordingly or get rid of the least important.

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Image Analyst
Image Analyst 2017-4-29
String them all together:
featureVector = [var1, var2, var3, ..... etc.]
It might help to normalize all variables to the range 0-1 so that one is not overly influential just because it has a higher value.
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Elias Unk
Elias Unk 2017-4-30
so i took this example
tb=[22.9 30.0 30.3 27.8 24.1 28.2 26.4 12.6 39.7 38.0];
normalized_V = tb/norm(tb);
I = mat2gray(tb);
to check the difference results i'll get and for normalized_v i got 0.2503 0.3280 0.3312 0.3039 0.2635 0.3083 0.2886 0.1377 0.4340 0.4154
for I i got 0.3801 0.6421 0.6531 0.5609 0.4244 0.5756 0.5092 0 1.0000 0.9373 which one should i use and why
Image Analyst
Image Analyst 2017-4-30
They do different things. Either might be okay. The thing is you want to avoid values in your feature vector that are like orders of magnitude different from each other.

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