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Repository of Image Databases with Augmented Features

Welcome to the Repository of Image Databases with Augmented Features. Here you can find ready to use with your classifier image databases with augmented image features. You may also augment the image features in your own image databases using the software tools found here.

Objectives:

  1. Software which allows users to augment the features of existing image databases to improve machine learning effectiveness and increase statistics.
  2. Links to download image databases with augmented features.

Contributors

This project is supported by the Department of Mathematics at Texas A&M University – Commerce. Please visit the department webpage for more information about Mathematics at Texas A&M University – Commerce.

Main Contributors

  • Nikolay Metodiev Sirakov a
  • Adam Bowden a

Collaborators

  • Mengzhe Chen
  • Oluwasey Ingbassani
  • Long Ngo b

a. Texas A&M University – Commerce

b. L2TI, University Sorbonne Paris Nord, France

Downloads

When using any results, software, or databases from this webpage, please cite the corresponding paper in which the results, software, or databases have been published (see reference section below), along with the following:

N.M. Sirakov, A. Bowden, T. Wang, J. Gamez, Repository of Image Databases with Augmented Features, https://www.tamuc.edu/projects/augmented-image-repository/?redirect=none

When using any databases linked here, cite the original databases [1, 5, 7] as well.

When using the ELPAC-S software, please include this citation:

A. Bowden, N.M. Sirakov, Active Contour Directed by the Poisson Gradient Vector Field and Edge Tracking, Journal of Mathematical Imaging and Vision – Springer, 63:665–680, 2021, https://rdcu.be/cflaI, (2021)

When using the SRWC software, include citation [8].

Sample Results and Metrics

Example skin lesion image with augmented features.
Example skin lesion image with augmented features fro ISIC 2020 [1].

Below are some sample results from the related research work demonstrating the effectiveness of augmenting database images with more features. More information can be found in papers [6, 8, 9] listed in the reference section below. In particular, the following table contains results from our paper [6].

ModelISIC 2020 [1] Original Dataset Accuracy (%)ISIC 2020 vû Augmented [6] Accuracy (%)Model COIL-100 [7] Original Dataset Accuracy (%)COIL-100 vû Augmented [6] Accuracy (%)
DenseNet 121 [2]89.4088.88DenseNet 12188.7287.88
ResNet 50 [3]90.2091.19ResNet 50 89.5190.16
Inception ResNet V2 [4]89.6092.50Inception ResNet V2 88.9191.45
EfficientNet B0 [5]91.6992.90EfficientNet B090.9991.85
SeyNet [6]90.1093.25SeyNet89.4191.27

Sample COIL-100 Images (click for larger images)

Vector Field vû

A house looking image
Car looking image
A boat looking image
A cylinder on a triangle.

Vector Field vϕ̂

A house looking image
Car looking image
A boat looking image
A cylinder on a triangle.

Acknowledgements

We would like to thank the following individuals for their continuing support of this work:

Supporting Publications

See papers [6, 8, 9] by the contributors which validate that augmenting image features increases machine learning effectiveness.

References

  1. International Skin Imaging Collaboration. SIIM-ISIC 2020 Challenge Dataset. International Skin Imaging Collaboration https://doi.org/10.34970/2020-ds01 (2020). [Creative Commons Attribution-Non Commercial 4.0 International License. The dataset was generated by the International Skin Imaging Collaboration (ISIC) and images are from the following sources: Hospital Clínic de Barcelona, Medical University of Vienna, Memorial Sloan Kettering Cancer Center, Melanoma Institute Australia, The University of Queensland, and the University of Athens Medical School. You should have received a copy of the license along with this work. If not, see https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt .]
  2. K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016 pp. 770-778. doi: 10.1109/CVPR.2016.90
  3. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q: Densely connected convolutional networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). pp. 22612269. doi: 10.1109/CVPR.2017.243
  4. C. Szegedy, S. Ioe, and V. Vanhoucke, Inception-v4, inception-resnet and the impact of residual connections on learning, Proc. of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), pp.4278-4284, (2017)
  5. T. A. Putra, S.I Rufaida, J-S Leu, Enhanced Skin Condition Prediction Through Machine Learning Using Dynamic Training and Testing Augmentation, DOI:10.1109/ACCESS.2020.2976045, IEEE Access, (2020).
  6. Igbasanmi, O., Sirakov, N.M., Bowden, A., CNN for Efficient Objects Classification with Embedded Vectors Fields, in printing by the Springer book series, Studies in Computational Intelligence, Electronic ISSN 1860-9503, Print ISSN 1860-949X, Best Paper Award
  7. “Columbia Object Image Library (COIL-100),” S. A. Nene, S. K. Nayar and H. Murase, Technical Report CUCS-006-96, February 1996.
  8. N.M. Sirakov, A. Bowden, M. Chen, L.H. Ngo, M. Luong, Embedding vector field into image features to enhance classification, Journal of Computational and Applied Mathematics, Vol. 441, 2024, 115685, ISSN 0377-0427, https://doi.org/10.1016/j.cam.2023.115685, https://www.sciencedirect.com/science/article/pii/S0377042723006283
  9. Oluwaseyi Dotum Igbasanmy, Nikolay Metodiev Sirakov, On the Usefulness of the Vector Field Singular Points Shapes for Classification, International Journal of Applied and Computational Mathematics, Springer, Accepted for publication January 12, 2024, DOI: https://doi.org/10.21203/rs.3.rs-2862010/v1
  10. HAM10000 Dataset: (c) by ViDIR Group, Department of Dermatology, Medical University of Vienna; https://doi.org/10.1038/sdata.2018.161
  11. MSK Dataset: (c) Anonymous; https://arxiv.org/abs/1710.05006; https://arxiv.org/abs/1902.03368
  12. MNIST Dataset: http://yann.lecun.com/exdb/mnist/
  13. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747
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