2026-10-12T05:11:52Z https://www.scidb.cn/oai

10.11922/sciencedb.j00104.00062 2020-10-17T16:19:46Z

2020-10-17 Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification doi:10.11922/sciencedb.j00104.00062 en One table and six figures. Table 1 shows the number of images for each label in the 1μ–2μ data set, adopting the same labelling used in [11, 12, 13], reported here for completeness: 0 = Porous sponges, 1 = Patterned surfaces, 2 = Particles, 3 = Films and coated surfaces, 4 = Powders, 5 = Tips, 6 = Nanowires, 7 = Biological, 8 = MEMS devices and electrodes, 9 = Fibres.Figure 1 shows test accuracy as a function of the number of training epochs obtained by training from scratch Inception-v3 (magenta), Inception-v4 (orange), Inception-Resnet (green), and AlexNet (black) on SEM data set. All the models were trained with the best combination of hyperparameters, according to the memory capability of the available hardware. In Figure 2, Main: Test accuracy as a function of the number of training epochs obtained when fine tuning on the SEM data set Inception-v3 (magenta) and Inception-v4 (orange) starting from the ImageNet checkpoint, and Inception-v3 (blue) from the SEM checkpoint that, as expected, converges very rapidly. Inset: Test accuracy as a function of the number of training epochs obtained when performing feature extraction of Inception-v3 (magenta), Inception-v4 (orange), and Inception-Resnet (green) on the SEM data set starting from the ImageNet checkpoint. All the models were trained with the best combination of hyperparameters, according to the memory capability of the hardware available. Figure 3 shows intrinsic Dimension of the 1μ–2μ_1001 data set, varying the sample size, computed before autoencoding (green lines) and after autoencoding (red lines). The three brightness levels for each color correspond to the percentage of points used in the linear fi t: 90%, 70%, and 50%. Figure 4 shows ddisc heatmap for a manually labelled subset of images. Figure 5 presents heatmaps of the distances obtained via Inception-v3. The image captions specify the methods used and indicate the correlation index with ddisc. Figure 6 shows NMI scores of the clustering obtained by the five hierarchical algorithms (solid lines) considered as a function of k, the number of clusters. The scores of the artificial scenarios are reported as orange (good case) and green (uniform case) dashed lines. Neural networks; Feature learning; Clustering analysis; Scanning Electron Microscope (SEM); Image classification Rossella Aversa Piero Coronica Cristiano De Nobili Stefano Cozzini PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1162/dint_a_00062 Science Data Bank