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Robust Classification of Strokes with SVM and Grouping

  • Gabriele Nataneli
  • Petros Faloutsos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4841)

Abstract

The ability to recognize the strokes drawn by the user, is central to most sketch-based interfaces. However, very few solutions that rely on recognition are robust enough to make sketching a definitive alternative to traditional WIMP user interfaces. In this paper, we propose an approach based on classification that given an unconstrained sketch, can robustly assign a label to each stroke that comprises the sketch. A key contribution of our approach is a technique for grouping strokes that eliminates outliers and enhances the robustness of the classification. We also propose a set of features that capture important attributes of the shape and mutual relationship of strokes. These features are statistically well-behaved and enable robust classification with Support Vector Machines (SVM). We conclude by presenting a concrete implementation of these techniques in an interface for driving facial expressions.

Keywords

Support Vector Machine Facial Expression Semantic Grouping Training Vector Shape Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Supplementary material

Supplementary material (70,410 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gabriele Nataneli
    • 1
  • Petros Faloutsos
    • 1
  1. 1.University of California Los Angeles 

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