Skip to main content
Log in

Ontology-based inference system for adaptive object recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Abdel-Mottaleb M, Elgammal A (1999) Face detection in complex environments from color images. Proc IEEE Int Conf Image Process 3:622–626

    Google Scholar 

  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. Bezdek JC, Li WQ, Attikiouzel Y, Windham M (1997) A geometric approach to cluster validity for normal mixtures. Soft Comput 1(4):166–179, Springer

    Article  Google Scholar 

  4. Celentano A, Gaggi O (2006) Context-aware design of adaptable multimodal documents. Multimed Tools Appl 29:7–28

    Article  Google Scholar 

  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 Recognition

  6. Cootes TF, Taylor CJ (2001) Statistical models of appearance for computer vision. University of Manchester, Manchester M13 9PT, UK

  7. Davis JW, Vakes S (2001) A perceptual user interface for recognizing head gesture acknowledgements. ACM workshop on perceptual user interfaces, pp 1–7

  8. Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Willey, New York

    MATH  Google Scholar 

  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 94143

  10. Gomez A, Fernandez M, Corch O (2004) Ontological engineering, 2nd edn. Springer, Berlin Heidelberg New York

    Google Scholar 

  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. 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. 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. 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, Springer

  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 843–852

  16. Kapoor A, Picard RW (2001) A real-time head nod and shake detector. In Proc. of the Workshop on Perceptive User Interfaces, pp 1–5

  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 40–45

  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. 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. 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. 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. 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. 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. 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):27–44

    Article  Google Scholar 

  25. Liu DH, Lam KM, Shen LS (2005) Illumination invariant object recognition. J Pattern Recognit 38:1705–1716

    Article  Google Scholar 

  26. Lumina RL, Shapiro G, Zuniga O (1983) A new connected components algorithm for virtual memory computers. Comput Vis Graph Image Process 22:287–300

    Article  Google Scholar 

  27. Ng CW, Ranganath S (2002) Real-time gesture recognition system and application. Image Vis Comput 20(13–14):993–1007, Elevier

    Article  Google Scholar 

  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. Phillips P (1999) The FERET database and evolution procedure for object recognition algorithms. Image Vis Comput 16(5):295–306, Elsevier

    Article  Google Scholar 

  30. Pitas I (1993) Digital image processing algorithms. Prentice Hall, Englewood Cliffs

    Google Scholar 

  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):513–531

    Article  Google Scholar 

  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. 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. Tan W, Rong G (2003) A real-time head nod and shake detector using HMMs. Expert Syst Appl 25:461–466

    Article  Google Scholar 

  35. Wang XT (2004) A unified framework for subspace object recognition. Proc IEEE Trans PAMI 26(9):1222–1228

    Article  Google Scholar 

  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):334–352

    Article  Google Scholar 

  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 China

Download references

Acknowledgment

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sung-Kwan Kang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kang, SK., Chung, KY. & Lee, JH. Ontology-based inference system for adaptive object recognition. Multimed Tools Appl 74, 8893–8905 (2015). https://doi.org/10.1007/s11042-013-1738-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1738-8

Keywords

Navigation