S EMISUPERVISED M ULTIVIEW D ISTANCE M ETRIC L EARNING FOR C ARTOON S YNTHESIS Jun Yu, Meng Wang,...
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Transcript of S EMISUPERVISED M ULTIVIEW D ISTANCE M ETRIC L EARNING FOR C ARTOON S YNTHESIS Jun Yu, Meng Wang,...
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