3.2.2 The Experimental Setup
To demonstrate the effectiveness of our proposed method, we conducted three sets of
experiments:
• Experiment 1: Training with grayscale image
• Experiment 2: Training with grayscale + lbp (radius 1, sampling 8 points)
• Experiment 3: Training with grayscale + lbp (radius 2, sampling 8 points).
3.3 The Experimental Results
The performance of the experimental results on the validation set is shown in Fig. 4.
We can see that combining LBP features when the model converges is better than using
only grayscale images, which proves the effectiveness of the proposed fusion method
between gray images and shallow texture features.
Table 2 shows the highest precision achieved in the validation set for Experiment 1,
Experiment 2, and Experiment 3.
Fig. 4. The performance of the three experiments on the validation set
Table 2. The highest precision achieved by the three experiments on the validation set
Model
Only gray
Gray + LBP (8, 1)
Gray + LBP (8, 2)
Val_acc (%)
78.91
79.42
79.64
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