SEMISUPERVISED MULTIVIEW DISTANCE METRIC LEARNING FOR CARTOON SYNTHESIS

Jun Yu, Meng Wang, Member, IEEE, and Dacheng Tao, Senior Member, IEEE

OUTLINE

Introduction Visual Feature Extraction for Character

Descriptions Semisupervised Multiview Distance Metric

Learning Results Conclusion

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 Semisupervised Multiview Distance Metric

Learning (SSM-DML)

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 semisupervised multiview distance metric learning (SSM-DML). SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement.

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.

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

VISUAL FEATURE EXTRACTION FOR CHARACTER DESCRIPTIONS

SEMISUPERVISED MULTIVIEW DISTANCE METRIC LEARNING

The traditional graph-based semi-supervised classification, named Local and Global Consistency (LLGC)

SEMISUPERVISED MULTIVIEW DISTANCE METRIC LEARNING

SEMISUPERVISED MULTIVIEW DISTANCE METRIC LEARNING

SEMISUPERVISED MULTIVIEW 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

RESULTS

RESULTS

RESULTS

RESULTS

RESULTS

RESULTS

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

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.

ENDTHANKS FOR LISTENING