Multimedia Tools and Applications

, Volume 74, Issue 20, pp 8893–8905 | Cite as

Ontology-based inference system for adaptive object recognition

  • Sung-Kwan KangEmail author
  • Kyung-Yong Chung
  • Jung-Hyun Lee


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.


Object recognition Context-awareness Context modeling Inference 



This work was supported by the Korea Foundation for the Advancement of Science & Creativity (KOFAC), and funded by the Korean Government (MOE).


  1. 1.
    Abdel-Mottaleb M, Elgammal A (1999) Face detection in complex environments from color images. Proc IEEE Int Conf Image Process 3:622–626Google Scholar
  2. 2.
    Baek SJ, Han JS, Chung KY (2013) Dynamic reconfiguration based on goal-scenario by adaptation strategy. Wirel Pers Commun. doi: 10.1007/s11277-013-1239-0 Google Scholar
  3. 3.
    Bezdek JC, Li WQ, Attikiouzel Y, Windham M (1997) A geometric approach to cluster validity for normal mixtures. Soft Comput 1(4):166–179, SpringerCrossRefGoogle Scholar
  4. 4.
    Celentano A, Gaggi O (2006) Context-aware design of adaptable multimodal documents. Multimed Tools Appl 29:7–28CrossRefGoogle Scholar
  5. 5.
    Chen Q, Wu H, Fukumoto T, Yachida M (1998) 3D head pose estimation without feature tracking. In Proc. of the IEEE International Conference on Automatic Face and Gesture RecognitionGoogle Scholar
  6. 6.
    Cootes TF, Taylor CJ (2001) Statistical models of appearance for computer vision. University of Manchester, Manchester M13 9PT, UKGoogle Scholar
  7. 7.
    Davis JW, Vakes S (2001) A perceptual user interface for recognizing head gesture acknowledgements. ACM workshop on perceptual user interfaces, pp 1–7Google Scholar
  8. 8.
    Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Willey, New YorkzbMATHGoogle Scholar
  9. 9.
    Ekman P, Huang T, Sejnowski T, Hager J (1993) Final report to NSF of the planning workshop on facial expression understanding. Technical report, National Science Foundation, Human Interaction Lab., UCSF, CA 94143Google Scholar
  10. 10.
    Gomez A, Fernandez M, Corch O (2004) Ontological engineering, 2nd edn. Springer, Berlin Heidelberg New YorkGoogle Scholar
  11. 11.
    Ha OK, Song YS, Chung KY, Lee KD, Park DJ (2013) Relation model describing the effects of introducing RFID in the supply chain: evidence from the food and beverage industry in South Korea. Pers Ubiquit Comput. doi: 10.1007/s00779-013-0675-x Google Scholar
  12. 12.
    Jung EY, Kim JH, Chung KY, Park DK (2013) Home health gateway based healthcare services through U-health platform. Wirel Pers Commun. doi: 10.1007/s11277-013-1231-8 Google Scholar
  13. 13.
    Kang SK, Chung KY, Lee JH (2013) Development of head detection and tracking systems for visual surveillance. Pers Ubiquit Comput. doi: 10.1007/s00779-013-0668-9 Google Scholar
  14. 14.
    Kang SK, Chung KY, Rim KW, Lee JH (2011) Development of real-time gesture recognition system using visual interaction. The International Conference IT Convergence and Security, LNEE 120, pp 295–306, SpringerGoogle Scholar
  15. 15.
    Kang SK, Chung KY, Rim KW, Lee JH (2012) Context-aware statistical inference system for effective object recognition. In Proc. of 2th International Conference IT Convergence and Security, Springer, pp 843–852Google Scholar
  16. 16.
    Kapoor A, Picard RW (2001) A real-time head nod and shake detector. In Proc. of the Workshop on Perceptive User Interfaces, pp 1–5Google Scholar
  17. 17.
    Kawato S, Ohya (2000) Real-time detection of nodding and head-shaking by directly detecting and tracking the between-eyes. In Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, pp 40–45Google Scholar
  18. 18.
    Kim JH, Chung KY (2013) Ontology-based healthcare context information model to implement ubiquitous environment. Multimed Tools Appl. doi: 10.1007/s11042-011-0919-6 Google Scholar
  19. 19.
    Kim SH, Chung KY (2013) 3D simulator for stability analysis of finite slope causing plane activity. Multimed Tools Appl. doi: 10.1007/s11042-013-1356-5 Google Scholar
  20. 20.
    Kim SH, Chung KY (2013) Medical information service system based on human 3D anatomical model. Multimed Tools Appl. doi: 10.1007/s11042-013-1584-8 Google Scholar
  21. 21.
    Kim GH, Kim YG, Chung KY (2013) Towards virtualized and automated software performance test architecture. Multimed Tools Appl. doi: 10.1007/s11042-013-1536-3 Google Scholar
  22. 22.
    Ko JW, Chung KY, Han JS (2013) Model transformation verification using similarity and graph comparison algorithm. Multimed Tools Appl. doi: 10.1007/s11042-013-1581-y Google Scholar
  23. 23.
    Lee JE, Lee KD, Chung KY, Gen M (2013) A multi-objective hybrid genetic algorithm to minimize the total cost and delivery tardiness in a reverse. Multimed Tools Appl. doi: 10.1007/s11042-013-1594-6 Google Scholar
  24. 24.
    Lee KD, Nam MY, Chung KY, Lee YH, Kang UG (2013) Context and profile based cascade classifier for efficient people detection and safety care system. Multimed Tools Appl 63(1):27–44CrossRefGoogle Scholar
  25. 25.
    Liu DH, Lam KM, Shen LS (2005) Illumination invariant object recognition. J Pattern Recognit 38:1705–1716CrossRefGoogle Scholar
  26. 26.
    Lumina RL, Shapiro G, Zuniga O (1983) A new connected components algorithm for virtual memory computers. Comput Vis Graph Image Process 22:287–300CrossRefGoogle Scholar
  27. 27.
    Ng CW, Ranganath S (2002) Real-time gesture recognition system and application. Image Vis Comput 20(13–14):993–1007, ElevierCrossRefGoogle Scholar
  28. 28.
    Oh SY, Chung KY (2013) Target speech feature extraction using non-parametric correlation coefficient. Clust Comput. doi: 10.1007/s10586-013-0284-5 Google Scholar
  29. 29.
    Phillips P (1999) The FERET database and evolution procedure for object recognition algorithms. Image Vis Comput 16(5):295–306, ElsevierCrossRefGoogle Scholar
  30. 30.
    Pitas I (1993) Digital image processing algorithms. Prentice Hall, Englewood CliffsGoogle Scholar
  31. 31.
    Qing L, Shan S, Gao W, Du B (2005) Object recognition under generic illumination based on harmonic relighting. Int J Pattern Recognit Artif Intell 19(4):513–531CrossRefGoogle Scholar
  32. 32.
    Shin DK, Jung H, Chung KY, Park RC (2013) Performance analysis of advanced bus information system using LTE antenna. Multimed Tools Appl. doi: 10.1007/s11042-013-1539-0 Google Scholar
  33. 33.
    Song CW, Lee D, Chung KY, Rim KW, Lee JH (2013) Interactive middleware architecture for Lifelog based context awareness. Multimed Tools Appl. doi: 10.1007/s11042-013-1362-7 Google Scholar
  34. 34.
    Tan W, Rong G (2003) A real-time head nod and shake detector using HMMs. Expert Syst Appl 25:461–466CrossRefGoogle Scholar
  35. 35.
    Wang XT (2004) A unified framework for subspace object recognition. Proc IEEE Trans PAMI 26(9):1222–1228CrossRefGoogle Scholar
  36. 36.
    Weiming H, Tieniu T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern Part C Appl Rev 34(3):334–352CrossRefGoogle Scholar
  37. 37.
    Yang T, Pan Q, Li J, Cheng Y, Zhao C (2004) Real-time head tracking system with an active camera. In Proc. of the World Congress on Intelligent Control and Automation, Hangzhou, PR ChinaGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sung-Kwan Kang
    • 1
    Email author
  • Kyung-Yong Chung
    • 2
  • Jung-Hyun Lee
    • 3
  1. 1.HCI Laboratory, Department of Computer and Information EngineeringInha UniversityIncheonSouth Korea
  2. 2.School of Computer Information EngineeringSangji UniversityWonju-siSouth Korea
  3. 3.Department of Computer and Information EngineeringInha UniversityIncheonSouth Korea

Personalised recommendations