北京市科委基金(D171100003717003)
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Figure 1
(Color online) Roc pattern in miao nationality. (a) Body of the fish; (b) wings of the bird; (c) body of the bird
Figure 2
Framework of national cultural pattern digitalization
Figure 3
(Color online) Research examples of national cultural pattern digitalization
Figure 4
(Color online) Framework of multi-label image annotation. (a) Represents image feature extraction; (b) constructs the dictionary; (c) and (d) are the test stages
Figure 5
(Color online) Examples of image annotation. (a) Traditional carpet pattern; (b) traditional national dress pattern; (c) Ming and Qing court dress pattern
Figure 6
Convergence of the objective function
Figure 7
(Color online) Hyper-parameters: (a) $\lambda_1$; (b) $\lambda_2$; (c) $\lambda_3$; (d) $\gamma$
| Data set | Number of | Number of | LCard | LDen | DL | PDL | Number of trianing |
| images | labels | samples | |||||
| Ming and Qing court dress pattern | 899 | 5 | 1.3582 | 0.2716 | 12 | 0.0133 | 600 |
| Traditional national dress pattern | 782 | 6 | 2.7724 | 0.4621 | 18 | 0.0230 | 400 |
| Traditional carpet pattern | 536 | 3 | 1.5690 | 0.5230 | 7 | 0.0131 | 200 |
| One-error | Coverage | Ranking-loss | Average-precision | |
| ML-KNN | 0.3946 | 1.1873 | 0.2152 | 0.7419 |
| Rank-SVM | 0.3679 | 1.1037 | 0.1909 | 0.7653 |
| MLNB | 0.5552 | 1.5418 | 0.3088 | 0.6533 |
| LLSF-BR | 0.4013 | 1.2642 | 0.2305 | 0.7340 |
| LLSF-CC | 0.7458 | 1.8428 | 0.3946 | 0.5427 |
| LLSF | 0.5686 | 1.6923 | 0.3464 | 0.6243 |
| SCMIDL | 0.3478 | 0.9565 | 0.1577 | 0.7911 |
| One-error | Coverage | Ranking-loss | Average-precision | |
| ML-KNN | 0.0700 | 1.7853 | 0.0586 | 0.9313 |
| Rank-SVM | 0.0497 | 1.7382 | 0.0550 | 0.9431 |
| MLNB | 0.0497 | 1.8508 | 0.0699 | 0.9321 |
| LLSF-BR | 0.0445 | 1.7304 | 0.0528 | 0.9459 |
| LLSF-CC | 0.0602 | 1.7592 | 0.0620 | 0.9385 |
| LLSF | 0.0524 | 1.8455 | 0.0667 | 0.9357 |
| SCMIDL | 0.0445 | 1.6780 | 0.0450 | 0.9524 |
| One-error | Coverage | Ranking-loss | Average-precision | |
| ML-KNN | 0.1380 | 0.8661 | 0.1887 | 0.9049 |
| Rank-SVM | 0.0675 | 0.7589 | 0.1196 | 0.9445 |
| MLNB | 0.2791 | 1.0327 | 0.3037 | 0.8341 |
| LLSF-BR | 0.0859 | 0.7827 | 0.1365 | 0.9351 |
| LLSF-CC | 0.0951 | 0.8393 | 0.1672 | 0.9225 |
| LLSF | 0.0767 | 0.7173 | 0.1043 | 0.9496 |
| SCMIDL | 0.0670 | 0.7366 | 0.0950 | 0.9530 |
| Data set | Original objective function | $\rm~\lambda_{2}=0$, $\rm~\lambda_{3}=0$, $\rm~\gamma=0$ | $\rm~\lambda_{2}=0$ | $\rm~\lambda_{3}=0$, $\rm~\gamma=0$ | |
| 3*Average-precision | 1 | 0.7865 | 0.7829 | 0.7862 | 0.7809 |
| 2 | 0.9459 | 0.9411 | 0.9467 | 0.9424 | |
| 3 | 0.9305 | 0.9277 | 0.9290 | 0.9277 | |
| 3*Coverage | 1 | 1.0736 | 1.0803 | 1.0702 | 1.0870 |
| 2 | 1.6571 | 1.6518 | 1.6492 | 1.6466 | |
| 3 | 0.8184 | 0.8244 | 0.8214 | 0.8244 | |
| 3*One-error | 1 | 0.3378 | 0.3445 | 0.3411 | 0.3478 |
| 2 | 0.0340 | 0.0628 | 0.0340 | 0.0602 | |
| 3 | 0.1018 | 0.1080 | 0.1049 | 0.1080 | |
| 3*Ranking-loss | 1 | 0.1826 | 0.1856 | 0.1826 | 0.1873 |
| 2 | 0.0509 | 0.0525 | 0.0499 | 0.0516 | |
| 3 | 0.1388 | 0.1435 | 0.1404 | 0.1435 |
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