Welcome to the Repository of Image Databases, here you can find ready to use, with your classifier, image databases with augmented image features and software to create your own augmented image databases.
Key Features
- Image databases, CIFAR, COIL100, ISIC20XX, MNIST, and OCID, with augmented image features.
- Sample images and statistics to validate that training CNNs with image databases with augmented features (singular points) boosts machine learning classification.
- Software that was applied to generate the databases with augmented features.
Recent Additions
- 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.
- New section with information about distilled image features along with sample images.
*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
- Nikolay Metodiev Sirakov a
- Adam Bowden a
Collaborators
- Alexander C. Poltzer
- Eluwumi Petrus-Nihi
- Long Ngo b
- Mengzhe Chen
- Oluwasey Ingbassani
a. East Texas A&M University
b. L2TI, University Sorbonne Paris Nord, France