Light-Ontology Classification for Efficient Object Detection using a Hierarchical Tree Structure

효과적인 객체 검출을 위한 계층적 트리 구조를 이용한 조명 온톨로지 분류

  • 강성관 (인하대학교 컴퓨터정보공학부) ;
  • 이정현 (인하대학교 컴퓨터정보공학부)
  • Received : 2012.10.25
  • Accepted : 2012.11.20
  • Published : 2012.11.30


This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure 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 ontology of tree structure. 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 of tree structure based on illumination criteria. After selecting the proper light-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.


Object Detection;Context-Awareness;Light-Ontology;Feature point Detection