Research Article
DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification
Table 4
Accuracy obtained for DeepLumina on benchmark texture dataset DTD.
| | RGB | Proposed method - DeepLumina |
| Pretrained models | RGB | RGB + Y | RGB + L | RGB + V | RGB + Y | ColorSpaces | RGB18 | YCbCr | L∗a∗b∗ | HSV | YIQ |
| MobileNet + SVM | 61.37 | 66.64 | 67.31 | 66.14 | 68.15 | ResNet50 + SVM | 67.23 | 72.47 | 72.10 | 70.77 | 73.01 | ResNet101 + SVM | 66.92 | 72.50 | 72.31 | 72.43 | 73.63 | DenseNet201 + SVM | 65.12 | 70.66 | 70.64 | 68.35 | 71.37 | AlexNet + SVM | 46.35 | 49.75 | 49.42 | 48.71 | 49.84 | VGG19 + SVM | 55.78 | 58.60 | 58.86 | 58.73 | 59.53 | Inceptionv3 + SVM | 65.20 | 70.04 | 71.30 | 69.81 | 71.18 | InceptionResNetv2 + SVM | 65.94 | 70.78 | 71.29 | 70.85 | 71.39 |
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Best values are shown in bold and they are obtained for the proposed Method DeepLumina for Luminance from the YIQ color model.
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