STAG: Semantic Image Annotation Using Relationships between Tags

Im, Dong-Hyuk; Park, Geun-Duk

doi:10.1109/icisa.2013.6579483

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Ontology-based image annotation has been studied extensively [1][2][3][4][5]8]. Image annotation using tags is mainly used for image search by identifying rankings and meanings of tags.…”

Section: Related Workmentioning

confidence: 99%

“…Likewise, "person" could be used as the subject and "cloth" as the object of the predicate "wears," to generate a semantically correct scene graph. Although image annotations were represented using the RDF model in [3,4], in these works the user manually inputs the subject, predicate and object. The uniqueness of the present study lies in our application of the RDF model to scene graph generation using a machine learning algorithm.…”

Section: Proposed System 31 Semantic Scene Graphmentioning

confidence: 99%

“…Thus, it becomes increasingly more imperative to effectively store and manage these large amounts of images. Hitherto, image annotation techniques able to efficiently search through large amounts of image content have been proposed [1][2][3][4][5]. These techniques represent images in several ways by storing semantic information describing the image content along with the images themselves.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a method of adding semantic information by incorporating the Resource Description Framework (RDF) model [8] into conventional scene graph generation is proposed in this study. Although several works in the relevant literature [2][3][4]9,10] also applied the RDF model to image content, these studies did not utilize deep learning technology-based models. The proposed method detects image objects and relations using deep learning and attempts to express them using an RDF model, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Scene Graph Generation Using RDF Model and Deep Learning

et al. 2021

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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.

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“…Ontology-based image annotation has been studied extensively [1][2][3][4][5]8]. Image annotation using tags is mainly used for image search by identifying rankings and meanings of tags.…”

Section: Related Workmentioning

confidence: 99%

Section: Proposed System 31 Semantic Scene Graphmentioning

confidence: 99%