Repository of Image Databases: Conclusion

Citing This Repository

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:

Sirakov NM, Bowden A. Image Databases with Features Augmented with Singular-Point Shapes to Enhance Machine Learning. Electronics. 2024; 13(16):3150. https://doi.org/10.3390/electronics13163150

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

When using any databases linked here, cite the original databases [1, 5, 7, 10-14, 17] as well.

When using the ELPAC-S software, please also 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].

Acknowledgements

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

Supporting Publications

See papers [6, 8, 9] by the contributors that 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: https://github.com/cvdfoundation/mnist
  • [13]. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. Han Xiao, Kashif Rasul, Roland Vollgraf. arXiv:1708.07747
  • [14]. CIFAR-10 Dataset: https://www.cs.toronto.edu/~kriz/cifar.html
  • [15]. Eluwumi F. Petrus-Nihi, Rumana Akther, Nikolay Metodiev Sirakov, Image Features and Image Dataset Augmentation for Skin Lesions Machine Learning Classification, MCO2025, Mentz, France, Special sessions Innovations for Health: From Acquisition to AI-Driven Analysis, June 4-6, 2025, Metz, France, In printing by “Lecture Notes in Networks and System-Springer-Artificial Intelligence”.
  • [16]. Eluwumi Folake Petrus-Nihi, Nikolay Metodiev Sirakov, Embedding a New Conjugate Gradient Vector Field to Augment Image Features and Enhance Machine Learning Classification, INFUS2025, July 29-31, Istanbul, Turkey, 2025. In printing by “Lecture Notes in Networks and Systems – Springer – Intelligent and Fuzzy Systems”.
  • [17]. Histopathologic Oral Cancer Detection using CNNs: https://www.kaggle.com/datasets/ashenafifasilkebede/dataset
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