Linked tag: image annotation using semantic relationships between image tags

Im, Dong-Hyuk; Park, Geun-Duk

doi:10.1007/s11042-014-1855-z

Cited by 21 publications

(11 citation statements)

References 16 publications

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“…Bouzaieni and Tabbone proposed an image annotation method based on the overview graph model [41]. Search-based annotation enables the automatic annotation of images through massive image retrieval and an analysis of the network [42], [43]. With the research on and development of the Semantic Web, many research results have been achieved through the Semantic Web-based analysis of annotation technology [44], [45].…”

Section: Related Workmentioning

confidence: 99%

Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

Zhang

Yang

2019

IEEE Access

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A large number of IoT applications require the use of supervised machine learning, a type of machine learning algorithm that requires data to be labeled before the model can be trained. Because manually labeling large datasets is a time-consuming, error-prone, and expensive task, automated machine learning methods can be used. To tackle the challenge in which an ancient character image needs to be manually labeled, this paper explores the classification method of ancient Chinese character images based on density peak clustering. We design a metric function of density peak clustering and propose an improved density peak clustering method based on information entropy for ancient book image classification. The method enumerates the distance threshold of clustering, then calculates the information entropy of the clustering result, and determines the class distance threshold by analyzing the attenuation of the information entropy, thereby completing the image clustering process. The improved metric function is used to calculate the similarity between images. A greedy strategy is used as the basis of the merging operation of the class members to achieve the purpose of increasing the degree of information entropy attenuation. The experimental results on the dataset of the Yi character images prove that the method can accurately classify unknown character images of ancient books. INDEX TERMS Density peak clustering, information entropy, ancient character image.

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Section: Related Workmentioning

confidence: 99%

Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

Zhang

Yang

2019

IEEE Access

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“…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%

“…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

Self Cite

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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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“…The paper by Park et al [7] proposes 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 context in which the image tags are used.…”

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confidence: 99%

Guest Editorial: Convergence and Advanced Technology

Ghose

Ryu

2015

Multimed Tools Appl

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Convergence and advanced technologies spring forth as a result of lateral entry by firms in different markets, and today, there is a synergistic combination of traditional industries and state-of-the-art technologies, such as information technology. The main goal of this issue is to bring researchers from academia together with professionals from different industries to share algorithms, solutions, and architectures as well as their experiences. We expect this special issue to be viewed as a continuum of products and as the lateral entry of research from different technological perspectives on digital management. The effort diffuses technological competencies and values within existing technologies' base information systems. The convergence process blurs traditional technological and sectorial boundaries and thus opens new avenues of technological innovation in ubiquitous technologies [1,4,6,10,15]. This issue challenges processing overhead in convergence and advanced technologies.The paper by Kim et al. [5] introduces an efficient mobility management scheme for convergence of mobile media multicast services in next-generation networks. The proposed scheme applies a method to quickly rejoin a relevant multicast stream by enabling the AIMS-MM system to collect multicast group information through a multicast group information handler located in the multicast router. The paper by Oh et al. [2] presents the development of a one-source multi-use cross-platform based on zero coding. This method is to research and develop a native application-based development tool that can perfectly satisfy both onesource-multi-use and cross-platform, and secure stable hardware control and execution speed.The paper by presents low-cost image indexing for a massive database. The proposed method is an identifier generation for indexing that is efficient and effective from the perspective of cost and indexing performance, in order to compose identifiers with high cardinality. The paper by Jo et al. [9] presents an access control system for secure extensible markup language (XML) telemedicine documents. This Multimed Tools Appl (2015) 74:2195-2200

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Linked tag: image annotation using semantic relationships between image tags

Cited by 21 publications

References 16 publications

Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

Improved Density Peak Clustering Based on Information Entropy for Ancient Character Images

Semantic Scene Graph Generation Using RDF Model and Deep Learning

Guest Editorial: Convergence and Advanced Technology

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