Research Article
DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification
Table 5
Accuracy obtained for DeepLumina Method on benchmark texture dataset FMD.
| | RGB | DeepLumina - proposed method |
| Pretrained models | RGB | RGB + Y | RGB + L | RGB + V | RGB + Y | ColorSpaces | RGB [18] | YCbCr | L ∗ a ∗ b ∗ | HSV | YIQ |
| MobileNet + SVM | 74.60 | 87.1 | 87.30 | 85.10 | 85.80 | ResNet50 + SVM | 81.60 | 88.45 | 89.70 | 87.85 | 88.80 | ResNet101 + SVM | 81.40 | 89.46 | 89.65 | 89.20 | 90.15 | DenseNet201 + SVM | 80.75 | 88.83 | 87.38 | 87.46 | 89.17 | AlexNet + SVM | 64.30 | 70.02 | 70.05 | 69.50 | 71.50 | VGG19 + SVM | 78.10 | 80.40 | 81.65 | 79.35 | 81.68 | Inceptionv3 + SVM | 76.60 | 88.70 | 88.15 | 87.55 | 89.80 | InceptionResNetv2 + SVM | 82.20 | 88.75 | 90.01 | 90.03 | 90.05 |
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The best values obtained for DeepLumina on the FMD dataset are indicated in bold.
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