Development and application of the semantic annotation framework for digital images
ISSN: 0264-0473
Article publication date: 25 October 2021
Issue publication date: 29 November 2021
Abstract
Purpose
The purpose of this paper is to semantically annotate the content of digital images with the use of Semantic Web technologies and thus facilitate retrieval, integration and knowledge discovery.
Design/Methodology/Approach
After a review and comparison of the existing semantic annotation models for images and a deep analysis of the characteristics of the content of images, a multi-dimensional and hierarchical general semantic annotation framework for digital images was proposed. On this basis, taking histories images, advertising images and biomedical images as examples, by integrating the characteristics of images in these specific domains with related domain knowledge, the general semantic annotation framework for digital images was customized to form a domain annotation ontology for the images in a specific domain. The application of semantic annotation of digital images, such as semantic retrieval, visual analysis and semantic reuse, were also explored.
Findings
The results showed that the semantic annotation framework for digital images constructed in this paper provided a solution for the semantic organization of the content of images. On this basis, deep knowledge services such as semantic retrieval, visual analysis can be provided.
Originality/Value
The semantic annotation framework for digital images can reveal the fine-grained semantics in a multi-dimensional and hierarchical way, which can thus meet the demand for enrichment and retrieval of digital images.
Keywords
Acknowledgements
The authors would like to thank Dr Christopher Khoo Soo Guan for his suggestions on the revision. This study was supported by the State Ket Program og the National Social Science Foundation of China (Grany No. 17ATQ001).
This paper is one of the research outputs of the project supported by the State Key Program of National Social Science Foundation of China “Research on Linked Data-based Semantic Publishing of Academic Articles and its Application (Grant No. 17ATQ001)”.
Compliance with ethical standards.
Conflicts of interest: The authors declare that they have no conflict of interest.
Citation
Chen, J. and Ou, S. (2021), "Development and application of the semantic annotation framework for digital images", The Electronic Library, Vol. 39 No. 6, pp. 824-845. https://doi.org/10.1108/EL-07-2021-0131
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited