Inference Scene Labeling by Incorporating Object Detection with Explicit Shape Model

  • Quan Zhou
  • Wenyu Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


In this paper, we incorporate shape detection into contextual scene labeling and make use of both shape, texture, and context information in a graphical representation. We propose a candidacy graph, whose vertices are two types of recognition candidates for either a superpixel or a window patch. The superpixel candidates are generated by a discriminative classifier with textural features as well as the window proposals by a learned deformable templates model in the bottom-up steps. The contextual and competitive interactions between graph vertices, in form of probabilistic connecting edges, are defined by two types of contextual metrics and the overlapping of their image domain, respectively. With this representation, a composite clustering sampling algorithm is proposed to fast search the optimal convergence globally using the Markov Chain Monte Carlo (MCMC). Our approach is applied on both lotus hill institute (LHI) and MSRC public datasets and achieves the state-of-art results.


Markov Chain Monte Carlo Object Detection Structural Object Competitive Edge Graph Vertex 
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 2011

Authors and Affiliations

  • Quan Zhou
    • 1
  • Wenyu Liu
    • 1
  1. 1.Dept. of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanPR China

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