Components of Image Retrieval Ontology: A Systematic Meta-Synthesis

Document Type : Research

Authors

1 Department of Knowledge and Information Science, Kharazmi University, Tehran, Iran

2 Department of Information Science, Shahed University, Tehran, Iran

3 Department of Information Systems, IranDoc, Tehran, Iran

Abstract
Purpose: Given the significant growth of digital images and the increasing need for their effective management and retrieval, semantic image retrieval has become one of the most important research areas. The use of ontology as an approach to bridge the semantic gap between low-level visual features and high-level conceptual meanings has attracted considerable scholarly attention. This study aimed to identify and synthesize the components, key factors, and effective frameworks in the design of ontology-based image retrieval systems.
Method: This study employed a meta-synthesis approach following the seven-step model of Sandelowski and Barroso (2007). The research population consisted of articles published in Web of Science and Scopus databases up to the end of 2024. Following a systematic search and application of inclusion and exclusion criteria, 29 relevant articles were selected for final analysis. Data were analyzed using open and axial coding methods, and validity and reliability were ensured through inter-coder agreement and test-retest techniques.
Findings: Analysis and synthesis of the 29 selected articles yielded 41 initial codes, which were categorized into 15 main components and five core concepts: content, visual, structural, person-centered, and temporal-spatial dimensions. Content components, with 12 codes, constituted the broadest conceptual area.
Conclusion: The findings demonstrate that reducing the semantic gap and improving image retrieval requires the simultaneous integration of these five core concepts. The proposed conceptual framework can serve as a roadmap for researchers and developers in designing and evaluating next-generation image retrieval systems.

Keywords


Aarabi Moghaddam, H., Motameni, A., & Otarkhani, A. (2024). Governance dimensions and sub-dimensions framework applicable to fintech industry. Human Information Interaction, 11(2). [In Persian]
Abazari, M., & Tayebi, H. (2017). A comparative study of Qajar women’s clothing before and after Nasser-Din Shah’s trip to Europe (Case study: Women’s traditional and modern clothing). PH, 7(13), 15–30. [In Persian]
Alimohammadzade, K., & Mohebi, S. (2013). Systematic review of research papers in the recent three decades on the “reasons of cesarean section” and population health management strategies in Iran. Women’s Strategic Studies, 16(61), 7–57. [In Persian]
Allani, O., Mellouli, N., Baazaoui Zghal, H., Akdag, H., & Ben Ghézala, H. (2015). A relevant visual feature selection approach for image retrieval. VISAPP (2), 377–384. https://doi.org/10.5220/0005306303770384
Alsmadi, M. K. (2020). Content-based image retrieval using color, shape and texture descriptors and features. Arabian Journal for Science and Engineering, 45(4), 3317–3330. https://doi.org/10.1007/s13369-020-04384-y
Asadi Ghadikolaee, O., Asadi, S., Noroozi Chakoli, A., & Ehsani, R. (2014). Radiology image retrieval on public and specific search engines. Journal of Healthcare Management Research (JHMR), 5(2). [In Persian]
Asim, M. N., Wasim, M., Khan, M. U. G., Mahmood, N., & Mahmood, W. (2019). The use of ontology in retrieval: a study on textual, multilingual, and multimedia retrieval. IEEE Access, 7, 21662-21686. https://doi.org/10.1109/ACCESS.2019.2897849
Baghdadi, P., & Dadvar, A. (2021). Social values and architectural structure of historical houses in the Qajar period of Yazd city from the point of view of space syntax. Geography and Regional Planning, 11(45), 1013–1030 [In Persian]
Derakhshandeh, S., Sepehr, F., Abazari, Z., & Neshaneh, N. (2023). Search engine-based image indexing in retrieving text-based and content-based indexed images using the Delphi technique. Journal of Studies in Library and Information Science, 15(2), 21–36. https://doi.org/10.22055/slis.2021.33777.1741 [In Persian]
Deserno, T. M., Antani, S., & Long, R. (2009). Ontology of gaps in content-based image retrieval. Journal of Digital Imaging, 22(2), 202–215. https://doi.org/10.1007/s10278-007-9092-x
Dinh, N. T., Nhi, N. T. U., Le, T. M., & Van, T. T. (2023). A model of image retrieval based on KD-Tree Random Forest. Data Technologies and Applications. https://doi.org/10.1108/DTA-06-2022-0247
Gowsikhaa, D., Abirami, S., & Baskaran, R. (2012). Construction of image ontology using low-level features for image retrieval. In 2012 International Conference on Computer Communication and Informatics, 1–7. https://doi.org/10.1109/ICCCI.2012.6158922
Hosseini Beheshti, M. S. (2014). Word construction, terminology and knowledge engineering. Iranian Institute of Science and Technology; Chapar. [In Persian]
Hwang, M., Kong, H., Baek, S., & Kim, P. (2007). A method for processing the natural language query in ontology-based image retrieval system. In International Workshop on Adaptive Multimedia Retrieval. https://doi.org/10.1007/978-3-540-71545-0_1
Hyvönen, E., Saarela, S., Styrman, A., & Viljanen, K. (2003). Ontology-based image retrieval. In WWW (Posters).
Hyvönen, E., Styrman, A., & Samppa, S. (2014). Ontology-based image retrieval. [Publication information incomplete].
Jiang, S., Huang, T., & Gao, W. (2004). An ontology-based approach to retrieve digitized art images. In IEEE/WIC/ACM International Conference on Web Intelligence (WI'04) (pp. 131–137). https://doi.org/10.1109/WI.2004.10034
Jiang, S.-Q., Du, J., Huang, Q.-M., Huang, T.-J., & Gao, W. (2005). Visual ontology construction for digitized art image retrieval. Journal of Computer Science and Technology, 20(6), 855–860. https://doi.org/10.1007/s11390-005-0855-x
Karimi, E., Babaei, M., & Hosseini Beheshti, M.S. (2019). The Study of Semantic and Ontological Features of Thesaurus and Ontology-based Information Retrieval Systems. Iranian Journal of Information Processing and Management, 34(4), 1585-1612. https://doi.org/10.35050/JIPM010.2019.015 [In Persian]
Lakshmi, V. R., Gerard, D., Santhanavijayan, A., & Radha, S. (2023). A hybrid classifier-based ontology-driven image tag recommendation framework for social image tagging.
Lee, S.-S., & Yong, H.-S. (2008). Ontosonomy: Ontology-based extension of folksonomy. In 2008 IEEE International Workshop on Semantic Computing and Applications (pp. 27–32). https://doi.org/10.1109/IWSCA.2008.36
Liaqat, M. (2013). Image classification and retrieval based on crisp and fuzzy ontology. In 2013 3rd IEEE International Conference on Computer, Control and Communication (IC4) (pp. 1–6). https://doi.org/10.1109/IC4.2013.6653737
Liu, H. (2012). Research of image retrieval system framework based on ontology and content. In Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis (Vol. 8349, pp. 138–144). https://doi.org/10.1117/12.920506
Mezaris, V., Kompatsiaris, I., & Strintzis, M. G. (2004). Region-based image retrieval using an object ontology and relevance feedback. EURASIP Journal on Advances in Signal Processing, 2004(6), 1–16. https://doi.org/10.1155/S1110865704401188
Minu, R. I., & Thyagharajan, K. K. (2012). Multimodal ontology search for semantic image retrieval. [Publication information incomplete].
Minu, R. I., & Thyagharajan, K. K. (2014). Semantic rule based image visual feature ontology creation. International Journal of Automation and Computing11(5), 489-499. https://doi.org/10.1007/s11633-014-0832-3
Minu, R. I., & Thyagharajan, K. K. (n.d.). Narrowing the semantic-gap using multi-modal ontology for semantic image retrieval. Advances in Communication Technology and Application.
Mohd Khalid, Y. I. A., Noah, S. A., & Abdullah, S. N. S. (2011). Towards a multimodality ontology image retrieval. In Visual Informatics: Sustaining Research and Innovations: Second International Visual Informatics Conference (IVIC 2011), Proceedings, Part II (pp. 382–393). Springer. https://doi.org/10.1007/978-3-642-25200-6_36
Naqvi, S. M. R., Ghufran, M., Varnier, C., Nicod, J. M., & Zerhouni, N. (2025). Enhancing semantic search using ontologies: A hybrid information retrieval approach for industrial text. Journal of Industrial Information Integration, 45, 100835. https://doi.org/10.1016/j.jii.2025.100835
Riad, A. M., Elminir, H. K., & Abd-Elghany, S. (2012). A literature review of image retrieval based on semantic concept. International Journal of Computer Applications, 40(11): 12-19. https://doi.org/10.5120/5008-7327
Sadoughi, F., Valinejadi, A., Hassanzadeh, H. M., Bouraghi, H., & Pasyar, P. (2012). The challenges of semantic retrieval of images and the modern application of thesauri. [Journal information incomplete]. [In Persian]
Sandelowski, M., Barroso, J., & Voils, C. I. (2007). Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Research in Nursing & Health, 30(1), 99–111. https://doi.org/10.1002/nur.20176
Shamsfard, M., & Abdollahzadeh Barforosh, A. (2002). Conceptual knowledge extraction from text using linguistic and semantic patterns. Cognitive Science Updates, 4(1), 48–66. [In Persian]
Shati, N. M., Khalid Ibrahim, N., & Hasan, T. M. (2020). A review of image retrieval based on ontology model. Journal of Al-Qadisiyah for computer science and mathematics, 12(1), 10. https://doi.org/10.29304/jqcm.2020.12.1.658
Shirahama, K., & Uehara, K. (2011). Utilizing video ontology for fast and accurate query-by-example retrieval. In 2011 IEEE Fifth International Conference on Semantic Computing (pp. 395–402). https://doi.org/10.1109/ICSC.2011.88
Simpson, M. S., You, D., Rahman, M. M., Antani, S. K., Thoma, G. R., & Demner-Fushman, D. (2012). Towards the creation of a visual ontology of biomedical imaging entities. AMIA Annual Symposium Proceedings, 866.
Soo, V.-W., Lee, C.-Y., Li, C.-C., Chen, S. L., & Chen, C.-C. (2003). Automated semantic annotation and retrieval based on sharable ontology and case-based learning techniques. In 2003 Joint Conference on Digital Libraries: Proceedings (pp. 61–72).
Soo, V.-W., Lee, C.-Y., Yeh, J. J., & Chen, C.-C. (2002). Using sharable ontology to retrieve historical images. In Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 197–198). https://doi.org/10.1145/544220.544261
Su, J.-H., Wang, B.-W., Yeh, H.-H., & Tseng, V. S. (2009). Ontology-based semantic web image retrieval by utilizing textual and visual annotations. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (Vol. 3, pp. 425–428). https://doi.org/10.1109/WI-IAT.2009.317
Tavosi,M , Naghshineh,N , Zerehsaz,M and Mahboub,S . (2024). Proposing a conceptual framework for the aesthetics of images on the web: Meta-synthesis and ranking components identified. Human and Information Interaction, 11(3), 40-70. [In Persian]
Vagena, Z., Wei, X., Kurtz, C., & Cloppet, F. (2025). Semantic aware representation learning for optimizing image retrieval systems in radiology. Pattern Recognition, 158, 111060. https://doi.org/10.1016/j.patcog.2024.111060
Vijayarajan, V., & Dinakaran, M. (2016). A review on ontology-based document and image retrieval methods. Indian Journal of Science and Technology, 9(47), 1–13. https://doi.org/10.17485/ijst/2016/v9i47/86817
Wang, H., Liu, S., & Chia, L. T. (2006, October). Does ontology help in image retrieval? A comparison between keyword, text ontology and multi-modality ontology approaches. In Proceedings of the 14th ACM International Conference on Multimedia (pp. 109–112). https://doi.org/10.1145/1180639.1180672
Ye, J., Zhao, H., Li, S., Jin, C., & Wang, J. (2012). An ontology-based image retrieval system design and implementation. Information, 15(9), 3779.
Zhao, S., Potdar, V., & Chang, E. (2007). A practical image retrieval framework for tourism industry. In 2007 IEEE International Symposium on Industrial Electronics, 2928-2932. https://doi.org/10.1109/ISIE.2007.4375079