Transcript Image Deblurring and Denoising using Color Priors

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
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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_M7DBk8B
U
CONCLUSION
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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.
END
THANKS FOR LISTENING