Ontology-based inference system for adaptive object recognition

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• 作者：Sung-Kwan Kang ; Kyung-Yong Chung ; Jung-Hyun Lee
• 关键词：Object recognition ; Context ; awareness ; Context modeling ; Inference
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：74
• 期：20
• 页码：8893-8905
• 全文大小：1,475 KB
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• 作者单位：Sung-Kwan Kang (1)
  Kyung-Yong Chung (2)
  Jung-Hyun Lee (3)
  
  1. HCI Laboratory, Department of Computer and Information Engineering, Inha University, Yonghyeon 1,4-dong, Nam-gu, Incheon, 402-751, South Korea
  2. School of Computer Information Engineering, Sangji University, Usan-dong, Wonju-si, Gangwon-do, 220-702, South Korea
  3. Department of Computer and Information Engineering, Inha University, Yonghyeon 1,4-dong, Nam-gu, Incheon, 402-751, South Korea
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

This paper presents a statistical ontology approach for adaptive object recognition in a situation-variant environment. We propose a context model based on statistical ontology that is concentrated on object recognition. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we focused on designing a context-variant system using statistical ontology. Ontology, a collection of concepts and their interrelationships, provides an abstract view of an application domain. Researchers produce ontologies in order to understand and explain underlying principles and environmental factors. In this paper, we propose an ontology-based inference system for adaptive object recognition. The proposed method utilizes context ontology, context modeling, context adaptation, and context categorization to design the ontology based on illumination criteria for surveillance. After selecting the proper ontology domain, a set of actions is selected that produces better performance in that domain. We also carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, achieving enormous success that will enable us to proceed with our basic concepts. Keywords Object recognition Context-awareness Context modeling Inference