3 research outputs found
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation
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/SuperpixelGridMasksComment: The project is available at
https://github.com/hammoudiproject/SuperpixelGridMask
SuperpixelGridMasks Data Augmentation: Application to Precision Health and Other Real-world Data
ressources projet: https://github.com/hammoudiproject/SuperpixelGridMasksdatasets used for the article: 1 Dataset Chest X-Ray Images: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia2 A PASCAL VOC dataset: http://host.robots.ox.ac.uk/pascal/VOC/databases.html#VOC2005_1International audienceA 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 as well as precision health and surrounding real-world 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. SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks