Machine Learning and Deep Learning models make life easier when it comes to data processing and extracting relevant information out of it. However, a very prominent limitation of utilizing deep learning models for processing is that unless you have a huge data set, the creation of a learning model becomes extremely difficult. Having said that, manually processing even seemingly small documents is easier said than done. Thus, one thing is clear that we would require a learning model to get the job done, but the question is how.
To resolve this issue, a concept was developed where additional data is created from the existing sets, thereby creating more information to feed the deep learning model such that it can be implemented to process our data. This technique is called Image Augmentation. To know how this methodology works and why it is becoming increasingly popular, keep reading till the end and learn everything you need to know about image/data augmentation.
As stated before, the process where existing data is modified or altered such that additional data gets created, which can then be used to conveniently create a deep learning model is called Image Augmentation.
The need for image augmentation arises because every time it is not possible to manually collect training data for a deep learning model. Therefore, by changing the form or orientation of the available data, we can create an expanded data set that can further be used to train the deep learning model for information processing. Since a single original image is used to create the expanded data set, all the components will have the same label.
Here are a few popular data augmentation techniques for images:
There can be multiple ways by which a single image can be used to create new entities which can expand the data set. The first and the most basic way is to rotate the image towards one side such that it becomes a new image as per orientation. The point here is that even after the rotating, the image of the subject essentially remains the same, and it will contain the same characters having the same features just with an altered orientation. So, you end up making a larger training sample so that even for that small single image of yours, you get to use an intelligent deep learning model to complete the processing stage.
Another method of creating a generalized model for image processing is image shifting. In image shifting, you simply shift the focus in the original image to some other part so that the subject remains visible, but the view becomes different. This way, you end up having a new image which can then be fed to the deep learning model as part of the training sample. For the imaging technique, the geometric transformation that takes place has to be tracked efficiently to know the difference between the original and the new position of every object and element of the image.
As the name suggests, by flipping the original image towards one side or upside down, you again create a new image for the training sample. The mechanism works similar to that of image rotation methods, just that here the orientation can only be changed along two axes’. So, the final image is either flipped towards the left or right or either upwards or downwards. Consequently, you have two kinds of flips, namely horizontal flip and vertical flip.
A technique that not only helps in increasing the size of the training sample but also improves the accuracy of the final deep learning model that you intend to create is called Image Noising. Noise is nothing but the additional distractions that are present in an image, where you already know what the focused object is. By introducing noise or noisy elements, you tend to create new images from the original image.
This way, you make the final deep learning model ready to extract and process relevant information even when unwanted elements are present beside the object in focus. So, image noising is not just an augmentation technique per se, but it also helps in making the deep learning model more robust.
Another technique that works on a similar principle as noising is called image blurring. Here the objective is to augment the original image but by introducing blurred components in it. The reason behind it is that it prepares the deep learning model for processing images of a variety of image qualities. Therefore, even if the source image is of poor quality, it should still be able to process it in an equally satisfactory way.
KlearStack offers comprehensive data processing solutions even if you have a small data set that may not be sufficient enough to create a relevant deep learning model. We use advanced deep learning data augmentation techniques to create customized learning models that can be used to study and process your data or images comprehensively.