image segmentation with vgg16 and randomforest

Question

according to the below link, the writer has implemented the image segmentation with vgg16 as the encoder and the random forest as the classifier. https://github.com/bnsreenu/python_for_microscopists/blob/master/159b_VGG16_imagenet_weights_RF_for_semantic.py

As I know we need to take care of two concepts when implementing U-net : 1- Upsampling the extracted features in the decoder part 2- Adding residual connections to preserve the actual values of the input image.

Question 1:

How we can use only random forest instead of the decoder part of Unet?

Question 2:

I follow the tutorial video corresponding to the code(on youtube). The teacher sent the image to the encoder part and reshaped the extracted feature from (8,1024,996,64) to (8153292,64).

I am confused by this statement:

He says we have 64 features.

I think we should have 8153292*64 features. because I think any pixel is a feature

Klinsmann · Accepted Answer · 2023-08-16 17:37:56Z

0

Q1. VGG16 is used to encode the image (contracting path of the U-Net). Once the features extracted, you can use any classifier you want to classify the pixel as 0 or 1, or you can reconstruct a segmentation mask using the expansive path of the U-Net. The strategy used in your link is to use a random forest classifier.

Q2. After the contracting path of the U-Net, each of the 8153292 pixels is now represented by 64 features, instead of 3 (R,G,B).

answered Aug 16, 2023 at 17:37

Klinsmann

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