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  • Research Article
  • Open Access

Unsupervised Modeling of Objects and Their Hierarchical Contextual Interactions

EURASIP Journal on Image and Video Processing20092009:184618

  • Received: 11 June 2008
  • Accepted: 2 September 2008
  • Published:


A successful representation of objects in literature is as a collection of patches, or parts, with a certain appearance and position. The relative locations of the different parts of an object are constrained by the geometry of the object. Going beyond a single object, consider a collection of images of a particular scene category containing multiple (recurring) objects. The parts belonging to different objects are not constrained by such a geometry. However, the objects themselves, arguably due to their semantic relationships, demonstrate a pattern in their relative locations. Hence, analyzing the interactions among the parts across the collection of images can allow for extraction of the foreground objects, and analyzing the interactions among these objects can allow for a semantically meaningful grouping of these objects, which characterizes the entire scene. These groupings are typically hierarchical. We introduce hierarchical semantics of objects (hSO) that captures this hierarchical grouping. We propose an approach for the unsupervised learning of the hSO from a collection of images of a particular scene. We also demonstrate the use of the hSO in providing context for enhanced object localization in the presence of significant occlusions, and show its superior performance over a fully connected graphical model for the same task.


  • Pattern Recognition
  • Computer Vision
  • Graphical Model
  • Superior Performance
  • Single Object

Publisher note

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Authors’ Affiliations

Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA


© D. Parikh and T. Chen. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.