1D Haar wavelet transform

how to Decompose query image using Haar Wavelet
transformation at 1st level
then I want to Calculate mean and standard deviation of each coefficient and form one dimensional feature vector.
I don't know how to use wavelet Toolbox

Answers (1)

Wayne King
Wayne King on 15 Dec 2013
Edited: Wayne King on 15 Dec 2013
If you are sure you just want the 1st-level (and no further), you can use dwt2.
If you need multiple levels, use wavedec2().
However, your statement " mean and standard deviation of each coefficient and form one dimensional feature vector." does not make sense.
Do mean the mean and standard deviation of each subband image?
You can easily obtain the subband images
load woman;
dwtmode('per');
[CA,CH,CV,CD] = dwt2(X,'haar');
CH, CV, and CD are the wavelet subband images. Now can you point me to some text which describes how to find your feature vector from those matrices?

6 Comments

Decompose query image using Haar Wavelet transformation at 1st level to get approximate coefficient and vertical, horizontal and diagonal detail coefficients.

After a one-level wavelet transform, the wavelet coefficients is ci, j at the point (i, j), then the mean and standard deviation of any band are calculated as:μ =1/
The above is incomplete: "then the mean and standard deviation of any band are calculated as:μ =1/ "
shima said
shima said on 15 Dec 2013
Edited: shima said on 15 Dec 2013

I am sorry I sent it incomplete y mistake

After a one-level wavelet transform, the wavelet coefficients is ci, j at the point (i, j), then the mean and standard deviation of any band are calculated as:μ =1/
It is still not complete. What you posted is no different than your previous incomplete post.
shima said
shima said on 15 Dec 2013
Edited: shima said on 15 Dec 2013
I am very sorry I don't konw how did that happe :( i copied it and when i sent it looked like that
shima commented,
I want to containing the mean value and the variance of each image's coefficients.

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Asked:

on 15 Dec 2013

Commented:

on 18 Dec 2013

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