Research Article
An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics
Table 4
Quantitative evaluation of recognition results based on the first experimental datasets.
| | KNN | SVM | Our approach | Precision | Recall | Precision | Recall | Precision | Recall |
| Residential buildings | 0.5852 | 0.6132 | 0.6877 | 0.6523 | 0.9688 | 0.9798 | Commercial buildings | 0.6721 | 0.5547 | 0.7621 | 0.6788 | 0.9167 | 0.9136 | Circular roads | 0.3894 | 0.6998 | 0.5586 | 0.5919 | 0.9705 | 0.9814 | Swimming pool | 0.702 | 0.3485 | 0.8775 | 0.9262 | 0.9333 | 1 | Trees (1.5–5m) | 0.5264 | 0.4416 | 0.7295 | 0.8562 | 0.8824 | 0.9525 | Trees (5–10 m) | 0.4799 | 0.5230 | 0.7462 | 0.8025 | 0.8493 | 0.9729 | Trees (>10 m) | 0.5012 | 0.4611 | 0.7119 | 0.837 | 0.8677 | 0.9702 |
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