CODEWITHSUNDEEP
machine-learningcomputer-visionneural-networkdeep-learningcaffe
Biba
Biba

Reputation: 1595

Image preprocessing mean image subtraction

I have a question regarding the preprocessing step "Image mean subtraction".
I use the UCSD Dataset for my training.
One popular preprocessing step is the mean subtraction. Now I wonder if I am doing it right.
What I am doing is the following:

  1. I have 200 gray scaled Train images

  2. I put all images in a list and compute the mean with numpy: 

    np.mean(ImageList, axis=0)

    This returns me a mean image

  3. Now I subtract the mean image from all Train images

When I now visualize my preprocessed train images they are mostly black and have also negative values in them.
Is this correct? Or is my understanding of subtracting the mean image incorrect?

Here is one of my training images:

And this is the "mean image":

Upvotes: 3

Views: 3060

Answers (1)

Shai
Shai

Reputation: 114816

It seems like you are doing it right.
As for the negative values: they are to be expected. Your original images had intensity values in range [0..1], once you subtract the mean (that should be around ~0.5), you should have values in range (roughly) [-.5..0.5].

Please note that you should save the "mean image" you got for test time as well: once you wish to predict using the trained net you need to subtract the same mean image from the test image.

Update:
In your case (static camera) the mean subtracted removes the "common" background. These settings seems to be in your favor as they focus the net to the temporal changes in the frame. this method will work well for you, as long as you test on the same image set (i.e., frames from the same static camera).

Upvotes: 4

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