Image Deblurring and Denoising using Color Priors
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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
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
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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.
END
THANKS FOR LISTENING