Segmentation is essential for image analysis tasks. Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car).
Applications for semantic segmentation include:
Classification of terrain visible in satellite imagery
Medical imaging analysis
The steps for training a semantic segmentation network are as follows:
Large datasets enable faster and more accurate mapping to a particular input (or input aspect). Using data augmentation provides a means of leveraging limited datasets for training. Minor changes, such as translation, cropping, or transforming an image provides new distinct and unique images. See Augment Images for Deep Learning Workflows Using Image Processing Toolbox (Deep Learning Toolbox)
You can use the Image Labeler app to interactively label pixels and export the label data for training. The app can also be used to label rectangular regions of interest (ROIs) and scene labels for image classification.