Abstract
Many multimedia images, which are generated in countless ways, must be semantically stored and managed to search for relevant images. Therefore, studies on image annotation have been conducted and the field is developing at a steady pace. In existing approaches, the user inputs the tag for the image directly to obtain the semantic tag. In this study, the tag is input to the image using a convolutional neural network, which can be used for deep learning. Thus, it the hassle of entering tags for images is eliminated. Through deep learning, the user can automatically input the tag for the image, and the entered tags can be expanded to the resource description framework model in Linked Tag. Finally, the user can perform semantic-based image search using the SPARQL query language.
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Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1C1B1003600) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048380).
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Shin, Y., Seo, K., Ahn, J., Im, DH. (2020). Deep-Learning-Based Image Tagging for Semantic Image Annotation. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_10
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DOI: https://doi.org/10.1007/978-981-13-9341-9_10
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