How to make Neural Network Ignore the background?

I'm running deep learning on rocks, the trey the rocks sit on needs to be ignored, so I paint it a different color (neon green) and used a color thresholder generator to remove the background.

The problem is the 'removed' section still shows up as pixels on the image. So even when I 'remove' the background, the image itself still has all that empty space. I want the neural network to completely ignore it and only see the rocks.

Please help, for a really thorough solution I'll send you some bitcoin :)

[Jose Marques]

What kind of information you want from the neural networkw? Perhaps you do not have to remove the background (the code maybe learn even with the background)

[Simon MADEC]

There is a lot of possibilities, the best results may be achieved using mask RCNN. You can try this : https://github.com/jasjeetIM/Mask-RCNN

[Steven Moran]

I'm using the neural network to classify rocks. But if it sees the background it thinks the background is a rock and tries to classify it as such. I need it to completely ignore the background.

[Greg Heath]

It is not clear what your classes are. From your description I deduce

 class1 = tray
 class2 = rock

which probably is not correct.

So, please enlighten us.

Greg

[Image Analyst]

An image must remain rectangular so you can't really "remove" the pixels. You can color them a different color like blue or green or black, but they are still there if you want it to be a 2-D image, as opposed to just a 1-D list of color values. Please define exactly what "remove" means to you. Do you just mean "ignore"? Or do you think (incorrectly) that the "tray" pixels can somehow be thrown away so they do not even appear in the image at all?

[Steven Moran]

I want the neural network to ignore the background.

Apparently that simply means I need to train a 'class' as the background itself, since the background can't be 'removed' fully?

I have 6 classes of rock types. Apparently I need to make the background Tray as a class also?

Answer 1

[Greg Heath]

You don't need to remove the background.

 Just consider it another class.

Hope this helps.

Thank you for formally accepting my answer

Greg

[Steven Moran]

Ok I'll try it this weekend. I'll let you know how it goes.

Answer 2

[Helper]

If you want to classify different types of rock, then you do not have to remove the background.

One of the powerful place of Neural Network is it could extract useful feature automatically (namely, remove the useless information automatically). Generally, the first several layers of Neural Network work as feature extractor, and the following layers work as a classifier.

[Steven Moran]

one of my rock classes is very smooth and dark(like the background trey surface). that's why Im thinking I need to paint the background and set the background as its own class. I'm also splitting the screen into 300x300 pixel segments so get the exact percentage of each rock type present in the sample. When I segment it into smaller portions, it thinks the background is a smooth rock. That's why I need to paint it and set it as its own class I think.