Advertisement

A Novel Approach to Build Image Ontology Using Texton

  • R. I. Minu
  • K. K. Thyagarajan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)

Abstract

The mere existence of natural living thing can be studied and analyzed efficiently only by Ontology, where each and every existence are concern as entities and they are grouped hierarchically via their relationship. This paper deals the way of how an image can be represented by its feature Ontology though which it would be easier to analyze and study the image automatically by a machine, so that a machine can visualize an image as human. Here we used the selected MPEG 7 visual feature descriptor and Texton parameter as entity for representing different categories of images. Once the image Ontology for different categories of images is provided image retrieval would be an efficient process as through ontology the semantic of image is been defined.

Keywords

Ontology OWL RDFS MPEG7 Texton 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Minu, R.I., Thyagharajan, K.K.: Multimodal Ontology Search for Semantic Image Retrieval. Submitted to International Journal of Computer System Science & Engineering for February Issue (2012)Google Scholar
  2. 2.
    Nagarajan, G., Thyagharajan, K.K.: A Novel Image Retrieval Approach for Semantic Web. International Journal of Computer Applications (January 2012)Google Scholar
  3. 3.
    Minu, R.I., Thyagharajan, K.K.: Automatic image classification using SVM Classifier. CiiT International Journal of Data Mining Knowledge Engineering (July 2011)Google Scholar
  4. 4.
    Minu, R.I., Thyagharajan, K.K.: Scrutinizing Video and Video Retrieval Concept. International Journal of Soft Computing & Engineering 1(5), 270–275 (2011)Google Scholar
  5. 5.
    Nagarajan, G., Thyagharajan, K.K.: A Survey on the Ethical Implications of Semantic Web Technology. Journal of Advanced Reasearch in Computer Engineering 4(1) (June 2010)Google Scholar
  6. 6.
    Minu, R.I., Thyagharajan, K.K.: Evolution of Semantic Web and Its Ontology. In: Second Conference on Digital Convergence (2009)Google Scholar
  7. 7.
    Fan, J., Gao, Y., Luo, H.: Integrating concept ontology and Multitask learning to achieve more effective classifier training for multilevel image annotation. IEEE Transaction on Image Processing 17(3) (2008)Google Scholar
  8. 8.
    Penta, A., Picariello, A., Tanca, L.: Towards a definition of an Image Ontology. In: 18th Int. Workshop on Database and Expert Systems Applications (2007)Google Scholar
  9. 9.
    Hu, B., Dasmahapatra, S., Lewis, P., Shabolt, N.: Ontology-based medical image annotation with description logics. In: 15th IEEE Int. Conf. on Tools with Artificial Intelligence (2003)Google Scholar
  10. 10.
    Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: An Ontology ap-proach to object-based image retrieval (2003)Google Scholar
  11. 11.
    Maillot, N., Thonnat, M., Hudelot, C.: Ontology based object learning and recognition: Application to image retrieval (2004)Google Scholar
  12. 12.
    Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)CrossRefGoogle Scholar
  13. 13.
    ISO/IEC JTC1/SC29/WG11N6828 Palma de Mallorca, MPEG-7 Overview (version 10) (October 2004 ) Google Scholar
  14. 14.
    Bohring, H., Aure, S.: Mapping XML to OWL ontologies (2004)Google Scholar
  15. 15.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional texton. IJCV (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. I. Minu
    • 1
  • K. K. Thyagarajan
    • 2
  1. 1.Anna University of TechnologyTrichirappaliIndia
  2. 2.Dept. of Information & TechnologyRMK College of Engineering & TechnologyChennaiIndia

Personalised recommendations