CODEWITHSUNDEEP
pythonkerasimage-segmentationdata-augmentation
Mr.Engineer
Mr.Engineer

Reputation: 21

Data augmentation after image segmentation

Lets assume i have a little dataset. I want to implement data augmentation. First i implement image segmentation (after this, image will be binary image) and then implement data augmentation. Is this a good way?

Upvotes: 1

Views: 1414

Answers (2)

LazyAnalyst
LazyAnalyst

Reputation: 476

In case your dataset is small, you should add data-augmentation during the training. It is important to change the original image & the targets (masks) in the same way !!.

For example, If an image is rotated 90 degrees, then its mask should also be rotated 90 degrees. Since you are using Keras library, You should check if the ImageDataGenerator also changes the target images (masks), along with the inputs. If it doesn't, You can implement the augmentations by yourself. This repository shows how it is done in OpenCV here: https://github.com/kochlisGit/random-data-augmentations

Upvotes: 0

Younes
Younes

Reputation: 421

For image augmentation in segmentation and instance segmentation, you have to either no change the positions of the objects contained in the image by manipulating colors for example, or modify these positions by applying translations and rotation.

So, yes this way works, but you have to take into consideration the type of data you have and what you are looking to achieve. Data augmentation isn't a ready to-go process with good results everywhere.

In case you have a:

  • Semantic segmentation : Each pixel of your image has a row i and a column j which are labeled as its enclosing object. This means having your main image I and a label image L with its same size linking every pixel to its object label. In this case, your data augmentation is applied to both I and L, giving a combination of the two transformed images.
  • Instance segmentation : Here we generate a mask for every instance of the original image and the augmentation is applied to all of them including the original, then from these transformed masks we get our new instances.

EDIT: Take a look at CLoDSA (Classification, Localization, Detection and Segmentation Augmentor) it may help you implement your idea.

Upvotes: 1

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