Cropping is a fundamental image data augmentation technique used to artificially increase the size of a training dataset. By taking multiple random crops from a single source image, a data scientist can generate numerous new, slightly different training samples. This process not only expands the dataset but also helps the model improve its robustness and generalizability by learning to recognize objects in various positions and scales within the frame. This is particularly effective when the initial dataset is limited in size.

A. Clipping is a normalization process that limits pixel intensity values to a predefined range (e.g., 0-255); it does not create new training samples.

C. Masking involves obscuring parts of an image to train a model to handle occlusions, which modifies an image but does not typically generate new, distinct samples to increase dataset size.

D. Scaling resizes an entire image. While it can be used in an augmentation pipeline, it modifies the existing sample rather than creating new, unique samples from subsections of the original.

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1. Shorten

C.

& Khoshgoftaar

T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data

6(1)

60. In Section 3.1

"Geometric transformations

" the paper explicitly discusses cropping as a common data augmentation method where a section of the original image is taken to create a new sample. DOI: https://doi.org/10.1186/s40537-019-0197-0

2. Stanford University. (n.d.). CS231n: Convolutional Neural Networks for Visual Recognition

Course Notes

Data Augmentation. Stanford Computer Science. Retrieved from https://cs231n.github.io/transfer-learning/. The section on Data Augmentation details the practice of resizing an image and then taking random crops to generate multiple training examples from one image.

3. TensorFlow Authors. (n.d.). Core > Guides > Keras > Data augmentation. TensorFlow Official Documentation. Retrieved from https://www.tensorflow.org/tutorials/images/dataaugmentation. The official documentation provides code examples and explanations for using random cropping (tf.image.randomcrop) as a primary technique to augment an image dataset.