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Linked tag: image annotation using semantic relationships between image tags

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Abstract

State of the art image tagging systems are limited because they allow users to annotate image tags in noun form, which cannot fully express the semantics of image content. In this paper, we propose Linked Tag, a semi-automatic image annotation system that inserts semantic relationships between tags. The proposed annotation method connects image tags using predicate words that can capture the contexts in which the image tags are used. In particular, we exploit Linked Data such as DBPedia in order to connect the image tags with a property value. Compared with tag-based annotation and ontology-based annotation systems, Linked Tag eliminates a large amount of manual labor and enhances the semantic expression of image content. We also introduce two annotation-based applications on Linked Tag. First, we propose SPARQL query processing for image retrieval, which enables us to express visual appearance as well as semantic information. Second, we propose a novel tag-ranking algorithm based on the link analysis in the RDF annotation graph. Finally, we demonstrate the operation of our proposed system and analyze its efficacy.

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Notes

  1. http://dbpedia.org

  2. http://jena.apache.org

  3. http://flickr.com/groups/fruitandveg

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Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. NRF-2009-0078828).

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Authors and Affiliations

  1. Department of Computer and Information Engineering, Hoseo University, 20 Hoseo-roigil, Baebang-eup, Asan-si, Chungnam, 336-795, Republic of Korea

    Dong-Hyuk Im & Geun-Duk Park

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  1. Dong-Hyuk Im
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  2. Geun-Duk Park
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Correspondence to Geun-Duk Park.

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Im, DH., Park, GD. Linked tag: image annotation using semantic relationships between image tags. Multimed Tools Appl 74, 2273–2287 (2015). https://doi.org/10.1007/s11042-014-1855-z

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