Table 4.
Classification scores when deformation and varifold kernel widths are varied. Regularization of the covariance matrices ε = 10−2. Results are overall very stable when settings are varied. Very large kernel widths penalize the matching accuracy between the template and the subject shape complexes, thus eventually altering classification performance.
| LDA | ML | ||||
|---|---|---|---|---|---|
|
| |||||
| specificity | sensitivity | specificity | sensitivity | ||
| σV = 5 | σW = 2.5 | 98 (63/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) |
| σW = 5 | 98 (63/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |
| σW = 7.5 | 98 (63/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |
|
| |||||
| σV = 10 | σW = 2.5 | 98 (63/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) |
| σW = 5 | 98 (63/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |
| σW = 7.5 | 94 (60/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |
|
| |||||
| σV = 15 | σW = 2.5 | 89 (57/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) |
| σW = 5 | 83 (53/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |
| σW = 7.5 | 84 (54/64) | 100 (64/64) | 100 (64/64) | 100 (64/64) | |