Semisupervised Multiview Distance Metric Learning for Cartoon Synthesis

1. SemisupervisedMultiview Distance Metric Learning for Cartoon Synthesis Jun Yu, Meng Wang, Member, IEEE, and Dacheng Tao, Senior Member, IEEE

2. Outline • Introduction • Visual Feature Extraction for Character Descriptions • SemisupervisedMultiview Distance Metric Learning • Results • Conclusion

3. Introduction • Paperless system • MFBA algorithm • Graph based Cartoon Synthesis (GCS) system • Retrieval based Cartoon Synthesis (RCS) system • Unsupervised Bi-Distance Metric Learning (UB-DML) algorithm • SemisupervisedMultiview Distance Metric Learning (SSM-DML)

4. Introduction • 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 semisupervisedmultiview distance metric learning (SSM-DML).SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement.

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

6. Visual Feature Extraction for Character Descriptions • 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. Visual Feature Extraction for Character Descriptions

8. SemisupervisedMultiview Distance Metric Learning • The traditional graph-based semi-supervised classification, named Local and Global Consistency (LLGC)

9. SemisupervisedMultiview Distance Metric Learning

10. SemisupervisedMultiview Distance Metric Learning

11. SemisupervisedMultiview Distance Metric Learning • 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. Results

13. Results

14. Results

15. Results

16. Results

17. Results • http://www.youtube.com/watch?v=lR_M7DBk8BU

18. Conclusion • 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.

19. ENDThanks for listening