1. S EMISUPERVISED M ULTIVIEW D ISTANCE M ETRIC L EARNING FOR C ARTOON S YNTHESIS Jun Yu, Meng Wang, Member, IEEE, and Dacheng Tao, Senior Member, IEEE

2. O UTLINE Introduction Visual Feature Extraction for Character Descriptions Semisupervised Multiview Distance Metric Learning Results Conclusion

3. I NTRODUCTION Paperless system MFBA algorithm Graph based Cartoon Synthesis (GCS) system Retrieval based Cartoon Synthesis (RCS) system Unsupervised Bi-Distance Metric Learning (UB-DML) algorithm Semisupervised Multiview Distance Metric Learning (SSM-DML)

4. I NTRODUCTION They introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. They propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement.

5. I NTRODUCTION Distance metric Suppose we have a dataset X consisting of N samples x i (1 ≤ i ≤ N) in space Rm, i.e., X = [x1,..., xN] ∈ Rm×N.

6. V ISUAL F EATURE E XTRACTION FOR C HARACTER D ESCRIPTIONS Color Histogram - Color Histogram (CH) is an effective representation of the color information. Shape Context - The shape context descriptor is a way of describing the relative spatial distribution (distance and orientation) of the landmark points around feature points. Skeleton Feature - Skeleton, which integrates both geometrical and topological features of an object, is an important descriptor for object representation

7. V ISUAL F EATURE E XTRACTION FOR C HARACTER D ESCRIPTIONS

8. S EMISUPERVISED M ULTIVIEW D ISTANCE M ETRIC L EARNING The traditional graph-based semi-supervised classification, named Local and Global Consistency (LLGC)

9. S EMISUPERVISED M ULTIVIEW D ISTANCE M ETRIC L EARNING

11. Multiview Cartoon Character Classification -The module of multiview cartoon character classification is used as data preprocessing step, which clusters characters into groups specified by the users. Multiview Retrieval-Based Cartoon Synthesis -The main tasks of multiview retrieval based cartoon synthesis are character initialization and path drawing. Multiview Graph-Based Cartoon Synthesis

12. R ESULTS

17. http://www.youtube.com/watch?v=lR_M7DBk8B U

18. C ONCLUSION They investigate three visual features: color histogram, shape context and skeleton feature, to characterize the color, shape and action information of a cartoon character. The Experimental evaluations based on the modules of Multiview Cartoon Character Classification (Multi-CCC), Multiview Graph based Cartoon Synthesis (Multi-GCS) and Multiview Retrieval based Cartoon Synthesis (Multi-RCS) suggest the effectiveness of the visual features and SSM-DML.

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