Repository of Image Databases

Welcome to the Repository of Augmented Image Databases and Distilled and Retained after Pruning Sets. Here you can find, ready to use for training your classifier, databases of images with augmented features, distilled sets, and images retained after image database pruning. Software is also provided to create your own to augment your databases or distillations.

Key Features

1. Image databases, CIFAR, COIL100, ISIC20XX, MNIST, and OCID, with augmented image features.
2. Sample images and statistics to validate that training CNNs with image databases with augmented features (singular points) boosts machine learning classification.
3. Software that was applied to generate the databases with augmented features.

Recent Additions

• Distilled images from the CIFAR-10 databases (100 images with 10 per class), along with the code which was used to generate them.
• Distilled images from the databases: OCID (20 images) and digit-MNIST (100 images with 10 per class).
• Two new pruned image databases for Digit-MNIST* and Fashion-MNIST*.
• Two versions of the Oral Cancer Image Database (OCID) [17] embedded with vector field (VF) ̅∇ψ̂ sized 224×224* and 400×400*.
• OCID database with embedded new VF ̅∇g₁ = ∇(k₁ϕ̂ + k₂ψ̂) strings of singularities.

*The downloadable ZIP files (compressed folders) containing multiple images and data files; users will need to download and extract the files to access the contents.

Navigation

You may use the links in the Table of Contents to navigate to the different content sections.

Content areas include: Downloads, Images with Augmented Features, Distilled Images, and Citing This Work and References

Please visit the department webpage for more information about Mathematics at East Texas A&M University.

Founders

Collaborators