Human Body Segmentation with Multi-limb Error-Correcting Output Codes Detection and Graph Cuts Optimization

  • Daniel Sánchez
  • Juan Carlos Ortega
  • Miguel Ángel Bautista
  • Sergio Escalera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7887)

Abstract

Human body segmentation is a hard task because of the high variability in appearance produced by changes in the point of view, lighting conditions, and number of articulations of the human body. In this paper, we propose a two-stage approach for the segmentation of the human body. In a first step, a set of human limbs are described, normalized to be rotation invariant, and trained using cascade of classifiers to be split in a tree structure way. Once the tree structure is trained, it is included in a ternary Error-Correcting Output Codes (ECOC) framework. This first classification step is applied in a windowing way on a new test image, defining a body-like probability map, which is used as an initialization of a GMM color modelling and binary Graph Cuts optimization procedure. The proposed methodology is tested in a novel limb-labelled data set. Results show performance improvements of the novel approach in comparison to classical cascade of classifiers and human detector-based Graph Cuts segmentation approaches.

Keywords

Human Body Segmentation Error-Correcting Output Codes Cascade of Classifiers Graph Cuts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniel Sánchez
    • 1
  • Juan Carlos Ortega
    • 1
  • Miguel Ángel Bautista
    • 1
    • 2
  • Sergio Escalera
    • 1
    • 2
  1. 1.Dept. Matemàtica Aplicada i AnàlisiUBBarcelonaSpain
  2. 2.Centre de Visió per ComputadorCampus UABBarcelonaSpain

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