Deep learning is far superior to traditional machine learning with loads of training data. But, for limited training data traditional machine learning (e.g. Random Forest or SVM) may outperform deep learning. For image processing applications features need to be extracted / engineered for improved accuracy. Alternatively, features can be extracted from convolutional filters that are part of convolutional neural networks.
This video goes through the process of extracting features using convolutional filters and using them as inputs to a traditional Random Forest classifier to develop an image segmentation solution. You will find this approach to be easier to implement compared to U-net and you'll also find the results to be spectacular!!!
Code generated in the video can be downloaded from here:
github.com/bns...
The dataset used in this video can be downloaded from the link below. This dataset can be used to train and test machine learning algorithms designed for multiclass semantic segmentation. Please read the Readme document for more information.
drive.google.c...