CODEWITHSUNDEEP
imagematlabvectornormalize
user8179032
user8179032

Reputation:

How to normalize a vector used for features extraction from an image?

My actual vector has 110 elements that I'll use to extract features from images in matlab, I took this one (tb) to simplify

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);

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 if any of those 2 methods and why, and should I transform the features vector to 1 element after extraction for better training or leave it as a 110 element vector.

Upvotes: 0

Views: 654

Answers (1)

Víctor Martínez
Víctor Martínez

Reputation: 874

Normalization can be performed in several ways, such as the following:

  • Normalizing the vector between 0 and 1. In that case, just use:(tb-min(tb))/max(tb)
  • Making the maximum point at 1. In that case, just use: tb/max(tb) (which is the method that you have been used before).
  • Making the mean 0 and the standard deviation as 1. This is the most common method for using the returned values as features in a classification procedure and thus, I think that it is the one that you should use right now: zscore(tb) (or (tb-mean(tb))/std(tb)).

So, your final values would be:

zscore(tb)

ans =
-0.6664
 0.2613
 0.3005
-0.0261
-0.5096
 0.0261
-0.2091
-2.0121
 1.5287
 1.3066

Edit:

In regard to your second question, it depends on the number of observations. Every single classifier takes an MxN matrix of data and an Mx1 vector of labels as inputs. In this case, M refers to the number of observations, whereas N refers to the number of features. Usually, in order to avoid over-fitting, it is recommended to use a number of features less than the tenth part of the number of observations (i.e., the number of observations must be M > 10N).

So, in your case, if you use the entire 110-set of features, you should have a minimum of 1100 observations, otherwise you can have problems with over-fitting.

Upvotes: 1

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