Deep-Learning-Based Image Tagging for Semantic Image Annotation

Shin, Yoonmi; Seo, Kwangwon; Ahn, Jinhyun; Im, Dong-Hyuk

doi:10.1007/978-981-13-9341-9_10

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…Image tagging was performed using a CNN for image annotation in a previous study [10]. However, training images using a CNN makes it impossible to understand the semantics of objects.…”

Section: Creating a Deep Learning-based Scene Graphmentioning

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%

“…Second, deep learning technology was applied to the RDF model-based scene graphs. Although image content was also described in [3,10] using the RDF model, a user had to manually input the relation in [3], and a machine learning method was applied to detect only image tags in [10]. In this study, relations between image tags were detected using a deep learning model during generation of image scene graphs.…”

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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“…Image tagging was performed using a CNN for image annotation in a previous study [10]. However, training images using a CNN makes it impossible to understand the semantics of objects.…”

Section: Creating a Deep Learning-based Scene Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…The image description is a very important part of computer vision in modern science. The algorithms that somehow build a representation for the object are required in many scopes from image tagging and annotation for medical [1] or other purposes [2] to face verification [3]. The purpose of these methods is to transform an object (image, image patch, signal) into a vector of values.…”

Section: Introductionmentioning

confidence: 99%

Tiny CNN for feature point description for document analysis: approach and dataset

Sheshkus¹,

Chirvonaya²,

Arlazarov³

2022

Computer Optics

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In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset of photo and synthetically generated images and a method of training patches generation from it. We prove the effectiveness of this data by training a lightweight neural network and show how it performs in both general and documents patches matching. The training was done on the provided dataset in comparison with HPatches training dataset and for the testing, we solve HPatches testing framework tasks and template matching task on two publicly available datasets with various documents pictured on complex backgrounds: MIDV-500 and MIDV-2019.

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Deep-Learning-Based Image Tagging for Semantic Image Annotation

Cited by 2 publications

References 6 publications

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Tiny CNN for feature point description for document analysis: approach and dataset

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