  A novel approach of data augmentation based on irregular superpixel
decomposition is proposed. This approach called SuperpixelGridMasks permits to
extend original image datasets that are required by training stages of machine
learning-related analysis architectures towards increasing their performances.
Three variants named SuperpixelGridCut, SuperpixelGridMean and
SuperpixelGridMix are presented. These grid-based methods produce a new style
of image transformations using the dropping and fusing of information.
Extensive experiments using various image classification models and datasets
show that baseline performances can be significantly outperformed using our
methods. The comparative study also shows that our methods can overpass the
performances of other data augmentations. Experimental results obtained over
image recognition datasets of varied natures show the efficiency of these new
methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are
publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks
