• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.151-152
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• 2010

WWW images locate in structural, networking documents, so the importance of a word can be indicated by its location, frequency. There are two patterns for multi-cues ingegration annotation. The multi-cues integration algorithm shows initial promise as an indicator of semantic keyphrases of the web images. The latent semantic automatic keyphrase extraction that causes the improvement with the usage of multi-cues is expected to be preferable.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.1
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• pp.114-121
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• 2008

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic- based retrieval method can be available for various query of users. In this paper, we propose semantic-based video retrieval system for Environment Education Contents which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted form query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.95-102
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• 2004

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency md requires many efforts of system administrator or annotator because of imperfect automatic processing. In this paper, we propose semantics-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method and optimized comparison area extracting that propose. Therefore, we propose the system that can heighten retrieval efficiency of video data through semantics-based retrieval.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.203-216
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• 2004

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic- based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.999-1000
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• 2017

본 논문에서는 이전의 의미 기반 이미지 어노테이션 및 검색 시스템 Moment(Mobile Semantic Image Annotation and Retrieval System)에 RDF(Resource Description Framework) 추론 기능을 사용한 어노테이션 방법을 제안한다. 이를 위하여 제안된 시스템은 Apache Jena Inference API를 통해 구현되였으며 각 이미지들이 가진 어노테이션의 개수가 증가되었다. 자동으로 추론된 결과 또한 SPARQL 질의를 통해 검색이 가능하며, 기존 어노테이션 결과에 대한 의미 검색을 더욱 효과적으로 할 수 있게 한다.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.99-103
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• 2015

최근 딥러닝 기술의 발전에 힘입어 이미지로부터 자동으로 관련된 단어 혹은 문장을 생성하는 연구들이 진행되고 있는데, 많은 연구들은 이미지와 단어가 1:1로 대응된 잘 정련된 학습 집합을 필요로 한다. 한편 스마트폰 보급의 확산으로 인스타그램, 폴라 등의 이미지 기반 SNS가 급속하게 성장함에 따라 인터넷에는 한 이미지의 복수개의 단어(태그)가 부착되어있는 데이터들이 폭증하고 있는 것이 현실이다. 본 논문에서는 소규모의 잘 정련된 학습 집합뿐 아니라 이러한 대규모의 다중 레이블 데이터를 같이 활용하여 이미지로부터 태그를 생성하는 개선된 CNN구조 및 학습알고리즘을 제안한다. 기존의 분류 기반 모델에 은닉층을 추가하고 새로운 학습 방법을 도입한 결과, 어노테이션 성능이 기존 모델보다 11% 이상 향상되었다.

• Journal of radiological science and technology
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• v.28 no.1
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• pp.19-24
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• 2005

The purpose of this study was to design and implement a useful annotation-based Radiological image retrieval system to accurately determine on education and image information for Radiological technologists. For better retrieval performance based on large image databases, we presented an indexing technique that integrated $B^+-tree$ proposed by Bayer for indexing simple attributes and inverted file structure for text medical keywords acquired from additional description information about Radiological images. In our results, we implemented proposed retrieval system with Delphi under Windows XP environment. End users, Radiological technologists, are able to store simple attributes information such as doctor name, operator name, body parts, disease and so on, additional text-based description information, and Radiological image itself as well as to retrieve wanted results by using simple attributes and text keywords from large image databases by graphic user interface. Consequently proposed system can be used for effective clinical decision on Radiological image, reduction of education time by organizing the knowledge, and well organized education in the clinical fields. In addition, It can be expected to develop as decision support system by constructing web-based integrated imaging system included general image and special contrast image for the future.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.386-390
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• 2007

In this paper we present an Ontology Instance Management Tool. OntoManager is a user-friendly interactive ontology Instance management tool with webpage annotation tool and an image annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markup, creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. And OntoManager is an image annotation tool that allows users to markup regions of an image with respect to concepts in an ontology. It provides the functionality to import images, ontologies, instance bases, perform markup, and export the resulting annotations to disk or the Web.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.437-444
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• 2015

In this paper, we propose an information service system using augmented reality technology on mobile devices. With the advancement in ICT technology the computer is smaller and easy to carry and developed into the tablet PC and the smartphone typically. The user can confirm and learn the desired data using the augmented reality technology, regardless of the environment. Padding the supplementary images or videos to the real image using the camera, we can have help from such additionally obtained images. In this paper, using an augmented reality technology on a tablet PC or smartphone environment, we implement a system for providing information to the user. This system can be utilized in all areas such as learning, entertainment, public relations and advertisement, etc.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.