Table 1 Classification accuracy (%) of different approaches based on the Pascal VOC2007 dataset.
From: A Parallel Adaboost-Backpropagation Neural Network for Massive Image Dataset Classification
Image category | RF | SVM | Adaboost-BP32 | The method of Shi et al.10. | Parallel BP29 | The proposed approach |
|---|---|---|---|---|---|---|
plane | 81.9 | 82.5 | 82.6 | 87.2 | 90.0 | 94.1 |
bike | 80.0 | 79.9 | 79.8 | 83.3 | 86.2 | 90.5 |
bird | 78.2 | 78.4 | 78.4 | 83.9 | 87.1 | 92.6 |
boat | 79.9 | 80.1 | 80.5 | 85.0 | 87.6 | 94.3 |
btl | 55.0 | 54.2 | 54.6 | 57.1 | 63.2 | 73.6 |
bus | 72.8 | 73.2 | 73.1 | 79.6 | 82.9 | 87.3 |
car | 81.5 | 80.8 | 81.9 | 86.4 | 89.8 | 95.7 |
cat | 83.1 | 83.2 | 83.6 | 86.8 | 89.4 | 94.5 |
chair | 65.4 | 64.9 | 65.8 | 70.5 | 72.6 | 83.6 |
cow | 66.2 | 66.5 | 67.0 | 71.4 | 74.0 | 86.3 |
table | 60.1 | 60.0 | 60.3 | 65.2 | 70.5 | 81.6 |
dog | 84.3 | 84.1 | 84.7 | 88.4 | 90.4 | 96.5 |
horse | 81.9 | 82.1 | 82.4 | 87.3 | 91.3 | 95.9 |
moto | 79.0 | 79.2 | 79.3 | 83.8 | 87.2 | 92.8 |
pers | 86.5 | 86.9 | 87.3 | 93.5 | 95.7 | 97.1 |
plant | 58.3 | 59.1 | 60.2 | 66.1 | 71.9 | 78.6 |
sheep | 73.6 | 74.1 | 74.8 | 79.9 | 82.6 | 90.0 |
sofa | 63.5 | 63.9 | 65.7 | 68.3 | 73.8 | 79.9 |
train | 81.0 | 81.4 | 81.7 | 85.9 | 89.1 | 94.7 |
TV | 73.3 | 73.9 | 74.8 | 78.4 | 84.2 | 89.1 |
Mean | 74.3 | 74.4 | 74.9 | 79.4 | 83.0 | 89.4 |
Standard deviation | 9.314 | 9.377 | 9.213 | 9.400 | 8.576 | 6.634 |
