I am currently working on implementation of semantic segmentation of images neural network, and try to implement one of the already existing solution such as Fully Convolutional Neural Network 1.
Data that I am using is based on Pascal-Context dataset , which has additional labeling to original 20-class PASCAL VOC dataset. This results in a dataset with over 450-classes.
Initial 20-classes do not not match classes that I would like to achieve for indoor scenes. Therefore, I have created a short list of 12-classes that I would like to capture, which are in Pascal Context 450-classes dataset.
I managed to convert the data and now trying to start training. I am following this tutorial  on Matlab, which provides an example of an Image with classes overlay and all pixels colored.
However, in my scenario, I only want to be able to distinguish elements such as tvmonitor, sofa, wall and ignoring all other elements, which might be there. Matlab tutorial states: "Areas with no color overlay do not have pixel labels and are not used during training."[
As you can see above, I have two classes present in the picture, but I am not sure whether I should also include class Background, which would put overlay on everything that is not within my list of classes, and include that as an additional class in my training or not.
In summary, I am wondering whether Background needs to be provided as an additional class to my list of the classes that I would like to classify or not, even if this background class usually takes majority of each of images. Would that result in everything being classified as background?