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Context-Aware Statistical Inference System for Effective Object Recognition

  • Sung-Kwan Kang
  • Kyung-Yong Chung
  • Kee-Wook Rim
  • Jung-Hyun Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 215)

Abstract

This paper proposes a statistical ontology approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, statistical ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using statistical ontology. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology based on illumination criteria. After selecting the proper ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

Keywords

Object recognition Context-awareness Context modeling 

Notes

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (No. 2012-0004478).

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Sung-Kwan Kang
    • 1
  • Kyung-Yong Chung
    • 2
  • Kee-Wook Rim
    • 3
  • Jung-Hyun Lee
    • 4
  1. 1.HCI Laboratory, Department of Computer Science and EngineeringInha UniversityIncheonKorea
  2. 2.School of Computer Information EngineeringSangji UniversityWonju-siKorea
  3. 3.Department of Computer Science and EngineeringSunmoon UniversityAsan-siKorea
  4. 4.Department of Computer Science and EngineeringInha UniversityIncheonKorea

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