An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval

Authors

  • Mohd Suffian Sulaiman Centre for Computer Science Studies Universiti Teknologi MARA, Malaysia
  • Sharifalillah Nordin Centre for Computer Science Studies Universiti Teknologi MARA, Malaysia
  • Nursuriati Jamil Centre for Computer Science Studies Universiti Teknologi MARA, Malaysia

DOI:

https://doi.org/10.32890/jict2017.16.1.1

Keywords:

Multi-modality ontology, semantic image retrieval, object properties, semantic gap, object properties filter

Abstract

Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic. The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description. In other words, there is a difference between the computational representation in machine and human natural language. In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. To achieve this, a multi-modality ontology semantic image framework was proposed, comprising of four main components which were resource identification, information extraction, knowledge-based construction and retrieval mechanism. A new approach, namely object properties filter is proposed by customizing the semantic image retrieval algorithm and the graphical user interface to facilitate the user to engage with the machine i.e. computers, in order to enhance the retrieval performance. The experiment results showed that the proposed approach delivered better results compared to the approach that did not use the object properties filter based on probability precision measurement.

 

References

Broilo, M., & Natale, F. G. B. De. (2010). A stochastic approach to image retrieval using relevance feedback and particle swarm optimization. IEEE Transactions on Multimedia, 12(4), 267–277.

Chatzichristofis, S. A., Zagoris, K., Boutalis, Y. S., & Papamarkos, N. (2010). Accurate image retrieval based on compact composite descriptors and relevance feedback information. International Journal of Pattern Recognition and Artificial Intelligence, 24(02), 207–244. http://doi.org/10.1142/S0218001410007890

Commitee, M. H. M. (2009). Malaysian herbal monograph (Volume II). Kuala Lumpur, Malaysia: Forest Research Institute Malaysia (FRIM).

Commitee, M. H. M. (2012). Malaysian herbal monograph (Volume III). Shah Alam, Malaysia: Malaysian Herbal Monograph Commitee.

Cox, I. J., Miller, M. L., Minka, T. P., Papathomas, T. V., & Yianilos, P. N. (2000). The Bayesian image retrieval system. PicHunter: Theory, implementation and psychophysical experiments. IEEE Transactions on Image Processing, 9(1), 20–37. http://doi.org/10.1109/83.817596 Journal of ICT, 16, No. 1 (June) 2017, pp: 1–

Gruber, T. R. (1993). A translation approach to portable ontology specifications. Journal of Knowledge Acquisition, 5(2), 199–220.

Harris, J. G., & Harris, M. W. (2004). Plant identification terminology: An illustrated glossary (2nd ed.). Spring Lake Publishing.

Hoi, S. C. H., Lyu, M. R., & Jin, R. (2006). A unified log-based relevance feedback scheme for image retrieval. IEEE Transactions on Knowledge and Data Engineering, 18(4), 509–523. http://doi.org/10.1109/ TKDE.2006.1599389

Ismail, Z., Ismail, N., & Lassa, J. (1999). Malaysian herbal monograph (Volume I). Kuala Lumpur, Malaysia: Malaysian Monograph Committe.

Kesorn, K. (2010). Multi-modal mult-semantic image retrieval. (Doctoral Thesis, University of London). Retrieved from https://qmro.qmul. ac.uk/xmlui/handle/123456789/438

Khalid., Y. I. A. M., Azman, S., & Noah, M. (2012). Improving the performance of multi-modality ontology image retrieval system using DBpedia. Procedia Information Technology & Computer Science p. 1–9.

Khodaskar, A., & Ladhake, S. (2015). New-fangled alignment of ontologies for content-based semantic image retrieval. In Procedia Computer Science (Vol. 48, pp. 298–303). Elsevier Masson SAS. http://doi.org/10.1016/j. procs.2015.04.185

Kim, D., & Chung, C.-W. (2003). Qcluster : Relevance feedback using adaptive clustering for content-based image retrieval. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 599–610).

Kovashka, A., Parikh, D., & Grauman, K. (2015). WhittleSearch: Interactive image search with relative attribute feedback. International Journal of Computer Vision, 115(2), 185–210. http://doi.org/10.1007/s11263-015-0814-0

Kurita, T., & Kato, T. (1993). Learning of personal visual impression for image database systems. Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR ’93). http://doi.org/10.1109/ICDAR.1993.395676 Journal of ICT, 16, No. 1 (June) 2017, pp: 1–

Liu, Y., Zhang, D., Lu, G., & Ma, W. Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262–282. http://doi.org/10.1016/j.patcog.2006.04.045

M. Riad, A., K. Elminir, H., & Abd-Elghany, S. (2012). A literature review of image retrieval based on semantic concept. International Journal of Computer Applications, 40(11), 12–19. http://doi.org/10.5120/5008-7327

Magesh, N., & Thangaraj, P. (2011). Semantic image retrieval based on ontology and SPARQL query. International Journal of Computer Applications, 1(3), 12–16.

Mezaris, V., Kompatsiaris, I., & Strintzis, M. G. (2003). An ontology approach to object-based image retrieval. International Conference on Image Processing, ICIP (pp. 511–514).

Noy, N. F., & McGuinness, D. L. (2000). Ontology development 101 : A guide to creating your first ontology. Retrieved from http://protege.stanford. edu/publications/ontology_development/ontology101.pdf

Rocchio, J. J. (1965). Relevance feedback in information retrieval, 293-305. http://doi.org/10.1145/1463891.1463926

Rocchio, J. J. (1966). Document retrieval systems - optimization and evaluation. Harvard University.

Rui, Y., Huang, T. S., & Chang, S.-F. (1999). Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation, 10, 39–62.

Rui, Y., Huang, T. S., & Mehrotra, S. (1997). Content-based image retrieval with relevance feedback. Proceedings of Image Processing pp.815-818

Saian, R., & Ku-Mahamud, K. R. (2004). Searching malay text using stemming algorithm. Journal of Information and Communcation Technology (JICT), 3(2), 107–117. http://doi.org/10.1017/CBO9781107415324.

Serrano, N., Savakis, A. E., & Luo, J. (2004). Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition, 37(9), 1773–1784. http://doi.org/10.1016/j.patcog.2004.03. Journal of ICT, 16, No. 1 (June) 2017, pp: 1–

Singh, P., Goudar, R., Rathore, R., Srivastav, A., & Rao, S. (2013). Domain ontology-based efficient image retrieval. Proceedings of 7th International Conference on Intelligent Systems and Control.

Sivakamasundari, G., & Seenivasagam, V. (2012). Different relevance feedback techniques in cbir: a survey and comparative study. International Conference on Computing, Electronics and Electrical Technologies (pp. 1115–1121).

Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions On Pattern Analysis And Machine Intelligence, 22(12).

Su, J., Huang, W., & Yu, P. S. (2011). Efficient relevance feedback for content-based image retrieval by mining user navigation patterns. IEEE Transactions on Knowledge and Data Engineering, 23(3), 360–372.

Uschold, M., & Gruninger, M. (1996). Ontologies : principlesdd, methods and applications. Knowledge Engineering Review, 2, 1-53.

Wang, H., Liu, S., & Chia, L.-T. (2008). Image retrieval with a multi-modality ontology. Journal of Multimedia Systems, 13, 379–390. http://doi.org/10.1007/s00530-007-0099-

World Health Organisation (WHO). (2008). Traditional medicine. Retrieved from http://www.who.int/mediacentre/factsheets/fs134/en/#

Zhang, D., Islam, M. M., & Lu, G. (2012). A review on automatic image annotation techniques. Journal of Pattern Recognition, 45(1), 346–362. http://doi.org/10.1016/j.patcog.2011.05.

Zhang, Y. J. (2007). Semantic based visual information retrieval. IRM Press.

Zhou, X. S., & Huang, T. S. (2003). Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8, 536–544. http://doi.org/10.1007/s00530-002-0070-

Downloads

Published

31-05-2017

Issue

Vol. 16 No. 1 (2017): Journal of Information and Communication Technology (JICT) Vol.16, No.1, June 2017

Section

Articles

How to Cite

Sulaiman, M. S., Nordin, S., & Jamil, N. (2017). An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval. Journal of Information and Communication Technology, 16(1), 1-19. https://doi.org/10.32890/jict2017.16.1.1

Research impact

Harvested 2026-09-06
6 citations, from OpenAlex — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2017.16.1.1 OpenAlex W4230577324

Most read articles by the same author(s)