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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

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semisupervised multiview distance metric learning for cartoon synthesis

SemisupervisedMultiview Distance Metric Learning for Cartoon Synthesis

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

outline
Outline
  • Introduction
  • Visual Feature Extraction for Character Descriptions
  • SemisupervisedMultiview Distance Metric Learning
  • Results
  • Conclusion
introduction
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)
introduction1
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.
introduction2
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
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

semisupervised multiview distance metric learning
SemisupervisedMultiview Distance Metric Learning
  • The traditional graph-based semi-supervised classification, named Local and Global Consistency (LLGC)
semisupervised multiview distance metric learning3
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
results5
Results
  • http://www.youtube.com/watch?v=lR_M7DBk8BU
conclusion
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.