Semantic Scene Graph Generation Using RDF Model and Deep Learning
Open Access
- 17 January 2021
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 11 (2), 826
- https://doi.org/10.3390/app11020826
Abstract
Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.Funding Information
- Ministry of Science and ICT, South Korea (IITP-2020-2018-08-01417)
- Kwangwoon University (Research Resettlement Fund for the new faculty of Kwangwoon University in 2020)
This publication has 8 references indexed in Scilit:
- Improving the representation of image descriptions for semantic image retrieval with RDFJournal of Visual Communication and Image Representation, 2020
- Scene Graph Generation from Objects, Phrases and Region CaptionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- General Knowledge Embedded Image Representation LearningIEEE Transactions on Multimedia, 2017
- Deep Residual Learning for Image RecognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Linked tag: image annotation using semantic relationships between image tagsMultimedia Tools and Applications, 2014
- i-TagRanker: an efficient tag ranking system for image sharing and retrieval using the semantic relationships between tagsMultimedia Tools and Applications, 2011
- WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systemsComputers in Human Behavior, 2010
- Long Short-Term MemoryNeural Computation, 1997