Image Databases with Features Augmented with Singular-Point Shapes to Enhance Machine Learning
Abstract
:1. Introduction
2. Vector Fields That Augment Image Features
2.1. Definition of Vector Fields
2.2. Vector Field SPs for Image Feature Augmentation
3. Repository of Image Databases with Embedded VFs
3.1. Original Datasets
3.2. Image Datasets with Augmented Image Features
- ISIC2018-500 × 500- and ISIC2018-500 × 500-;
- ISIC2018-250 × 250- and ISIC2018-250 × 250-;
- ISIC2020-train- and ISIC2020-test-;
- ISIC2020-train- and ISIC2020-test-.
- ISIC2020-train- and ISIC2020-test-;
- ISIC2020-train- and ISIC2020-test-.
- COIL100-noise- and COIL100-noise-;
- ISIC2020-train-noise- and ISIC2020-test-noise-;
- ISIC2020-train-noise- and ISIC2020-test-noise-.
3.3. Software
- Segmenting via an evolving contour directed by the VF . To guide the active contour, with parameters customizable under “Contour Size and Shape”, a VF should be selected from the drop-down menu under “Vector Field Generation”. The recommended choice is “Nabla u Hat”.
- Splitting and tracing contours around multiple objects for full image segmentation. Such options are available under “Splitting Options”.
- Selecting any of the VFs from the “Vector Field Generation” drop-down menu. The six VFs described in Section 2 are at the top of the list. The first three of them have real-shaped SPs [1], while the next three have real- and complex-shaped SPs [3]. The selected VF will be embedded into the image file chosen using the Browse option.
4. Conclusions
- We defined the image regions where the different kinds of SPs will appear, as shown in Table 1.
- We determined the mappings between the SPs of the new VF and the SPs of the VF , formulated in [2, 3], as shown in Figure 8, if the two VFs are separately embedded into the same image.
- We defined a new type of image and image database, named “imprint of an image and imprint of an image database in a VF”.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ML | machine learning |
VF | vector field |
GVF | gradient vector field |
SP | singular points |
CP | critical points |
NN | neural network |
SRWC | sparse representation wavelet classification |
SRCQW | sparse representation classification quaternion wavelets |
CNN | convolutional NN |
SL | skin lesion |
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VF | SP/Location | SP/Location | SP/Location |
---|---|---|---|
saddle/core | sinking/concavity corners | ||
saddle/core, branches, concavities | sink/convex vertices, edges | spring/core | |
saddle/core, branches, concavities | sink/core | spring/edges, convex vertices | |
saddle/core | sink/core | spring/core; | |
spiral (in and out)/core | orbits/homogeneous regions | ||
, | saddle/core, convex vertices | sink/core, edges, branches | spring/core, edges, branches |
, | spiral (in and out)/core, concavities, branches | orbits/homogeneous regions |
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Sirakov, N.M.; Bowden, A. Image Databases with Features Augmented with Singular-Point Shapes to Enhance Machine Learning. Electronics 2024, 13, 3150. https://doi.org/10.3390/electronics13163150
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
Chicago/Turabian StyleSirakov, Nikolay Metodiev, and Adam Bowden. 2024. "Image Databases with Features Augmented with Singular-Point Shapes to Enhance Machine Learning" Electronics 13, no. 16: 3150. https://doi.org/10.3390/electronics13163150