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Class-Specified Segmentation with Multi-scale Superpixels

  • Han Liu
  • Yanyun Qu
  • Yang Wu
  • Hanzi Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7728)

Abstract

This paper proposes a class-specified segmentation method, which can not only segment foreground objects from background at pixel level, but also parse images. Such class-specified segmentation is very helpful to many other computer vision tasks including computational photography. The novelty of our method is that we use multi-scale superpixels to effectively extract object-level regions instead of using only single scale superpixels. The contextual information across scales and the spatial coherency of neighboring superpixels in the same scale are represented and integrated via a Conditional Random Field model on multi-scale superpixels. Compared with the other methods that have ever used multi-scale superpixel extraction together with across-scale contextual information modeling, our method not only has fewer free parameters but also is simpler and effective. The superiority of our method, compared with related approaches, is demonstrated on the two widely used datasets of Graz02 and MSRC.

Keywords

Vertical Edge Horizontal Edge Stochastic Gradient Descent Spatial Pyramid Conditional Random Field Model 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Han Liu
    • 1
  • Yanyun Qu
    • 1
  • Yang Wu
    • 2
  • Hanzi Wang
    • 3
  1. 1.Computer Science DepartmentXiamen UniversityChina
  2. 2.Academic Center for Computing and Media StudiesKyoto UniversityJapan
  3. 3.Center for Pattern Analysis and Machine IntelligenceXiamen UniversityChina

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