Table 3.
The correct classification percentages for data set 1 using local binary patterns and the method proposed in [8] with different k values in the k-NN classifier.
|
k |
||||||||
|---|---|---|---|---|---|---|---|---|
| Feature | P,R | 1 | 3 | 5 | 1 | 3 | 5 | |
| Before Weight | After Weight | |||||||
|
|
8,1 | 40.8 | 38.9 | 36.0 | 44.9 | 43.7 | 41.8 | |
| 16,2 | 81.6 | 80.3 | 72.6 | 83.0 | 82.4 | 75.9 | ||
| 24,3 | 88.6 | 86.9 | 83.1 | 88.9 | 87.9 | 83.9 | ||
|
| ||||||||
| 8,1+16,2 | 76.4 | 72.8 | 65.3 | 82.4 | 77.9 | 71.2 | ||
| 8,1+24,3 | 87.3 | 86.7 | 82.1 | 88.1 | 88.3 | 83.6 | ||
| 16,2+24,3 | 87.3 | 87.3 | 81.8 | 87.8 | 87.9 | 83.1 | ||
|
| ||||||||
| 8,1 + 16,2+24,3 | 86.5 | 85.8 | 81.2 | 88.0 | 88.6 | 82.6 | ||
|
| ||||||||
| Proposed method in [8] with db2 wavelet | e1 | 90.4 | 86.3 | 81.6 | 90.1 | 86.3 | 79.0 | |
| e2 | 56.9 | 55.7 | 56.5 | 68.2 | 68.1 | 68.0 | ||
| e1 & e2 | 81.6 | 79.6 | 77.1 | 92.1 | 88.1 | 84.3 | ||