• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.482-485
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• 2000

A modified watershed transform is proposed which is context-free marker-controlled and minima imposition-free to reduce the over-segmentation and to speedup the transform. In contrast to the conventional methods in which a priori knowledge, such as flat zones, zones of homogeneous texture, and morphological distance, is required for marker extraction, context-free marker extraction is proposed by using the attention operator based on the GST (generalized symmetry transform). By using the context-free marker, the proposed watershed transform exploit marker-constrained labeling to speedup the computation and to reduce the over-segmentation by eliminating the unnecessary geodesic reconstruction such as the minima imposition and thereby eliminating the necessity of the post-processing of region merging. The simulation results show that the proposed method can extract context-free markers inside the objects from the complex background that includes multiple objects and efficiently reduces over-segmentation and computation time.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.5
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• pp.557-565
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• 2014

This paper describes the extended pivot-based approach for bilingual lexicon extraction. The basic features of the approach can be described as follows: First, the approach builds context vectors between a source (or target) language and a pivot language like English, respectively. This is the same as the standard pivot-based approach which is useful for extracting bilingual lexicons between low-resource languages such as Korean-French. Second, unlike the standard pivot-based approach, the approach looks for similar context vectors in a source language. This is helpful to extract translation candidates for polysemous words as well as lets the translations be more confident. Third, the approach extracts translation candidates from target context vectors through the similarity between source and target context vectors. Based on these features, this paper describes the extended pivot-based approach and does various experiments in a language pair, Korean-French (KR-FR). We have observed that the approach is useful for extracting the most proper translation candidate as well as for a low-resource language pair.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.05a
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• pp.262-263
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• 2005

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.9-13
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• 2020

관계 추출은 주어진 문장이나 문서에 존재하는 개체들 간의 의미적 관계를 찾아내는 작업을 말한다. 최근 문서 수준 관계 추출 말뭉치인 DocRED가 공개되면서 문서 수준 관계 추출에 대한 연구가 활발히 진행되고 있다. 또한 사전 학습된 Masked Language Model(MLM)이 자연어처리 분야 전체에 영향력을 보이면서 관계 추출에서도 MLM을 사용하는 연구가 진행되고 있다. 그러나 문서 수준의 관계 추출은 문서의 단위가 길기 때문에 Self-attention을 기반으로 하는 MLM을 사용하면 모델의 계산량이 증가하는 문제가 있다. 본 논문은 이 점을 보완하기 위해 관계 추출에 필요한 문장을 선별하는 간단한 전처리 방법을 제안한다. 또한 문서의 길이에 상관없이 관계 추출에 필요한 어휘 정보를 자동으로 습득 할 수 있는 Relation-Context Co-attention 방법을 제안한다. 제안 모델은 DocRED 말뭉치에서 Dev F1 62.01%, Test F1 59.90%로 높은 성능을 보였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.939-948
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• 2005

In EBCOT, the context extraction process takes excessive calculation time and this paper proposed a method to reduce this calculation time. That is, if a coefficient is less than a pre-defined threshold value the coefficient and its descendents skip the context extraction process. There is a trade-off relationship between the calculation time and the image quality or the amount of output data such that as this threshold value increases, the calculation time and the amount of output data decreases, but the image degradation increases. Therefore, by deciding this threshold value according to the network environments or conditions, it is possible to establish a network-adaptive context extraction method. The experimental results showed that the range of the threshold values for acceptable image quality(better than 30dB) is from 0 to 4. The experimental results showed that in this range the Resulting reduction rate in calculation time was from $3\%\;to\;64\%$ in average, the reduction rate in output data was from $32\%$ to $73\%$ in average, which means that large reduction in calculation time and output data can be obtained with a cost of an acceptable image quality degradation. Therefore, the proposed method is expected to be used efficiently in the application area such as the real-time image/video data communication in wireless environments, etc.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.202-208
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• 2001

One of the most frequent uses of Internet is data gathering. Data can be about many themes but perhaps one of the most demanded fields is the tourist information. Normally, databases that support these systems are maintained manually. However, there is other approach, that is, to extract data automatically, for instance, from textual public information existing in the Web. This approach consists of extracting data from textual sources(public or not) and to serve them totally or partially to the user in the form that he/she wants. The obtained data can maintain automatically databases that support different systems as WAP mobile telephones, or commercial systems accessed by Natural Language Interfaces and others. This process has three main actors. The first is the information itself that is present in a particular context. The second is the information supplier (extracting data from the existing information) and the third is the user or information searcher. This added value chain reuse and give value to existing data even in the case that these data were not tough for the last use by the use of the described technology. The main advantage of this approach is that it makes independent the information source from the information user. This means that the original information belongs to a particular context, not necessarily the context of the user. This paper will describe the application based on this approach developed by the authors in the FLEX EXPRIT IV n$^{\circ}$EP29158 in the Work-package "Knowledge Extraction & Data mining"where the information captured from digital newspapers is extracted and reused in tourist information context.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.685-694
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• 2022

Building information is essential for various urban spatial analyses. For this reason, continuous building monitoring is required, but it is a subject with many practical difficulties. To this end, research is being conducted to extract buildings from satellite images that can be continuously observed over a wide area. Recently, deep learning-based semantic segmentation techniques have been used. In this study, a part of the structure of the context-based ResU-Net was modified, and training was conducted to automatically extract a building from a 30 cm Worldview-3 RGB image using SpaceNet's building v2 free open data. As a result of the classification accuracy evaluation, the f1-score, which was higher than the classification accuracy of the 2nd SpaceNet competition winners. Therefore, if Worldview-3 satellite imagery can be continuously provided, it will be possible to use the building extraction results of this study to generate an automatic model of building around the world.

• Journal of applied mathematics & informatics
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• v.40 no.1_2
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• pp.289-303
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• 2022

This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.185-191
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• 2011

Recently, the surveillance system is highly being attention. Various Technologies as detecting object from image than determining and recognizing if the object are person are universally being used. Therefore, In this paper shows detecting on this kind of object and local context based facial feather detection algorithm is being advocated. Detect using Gabor Bunch in the same time Bayesian detection method for revision to find feather point is being described. The entire system to search for object area from image, context-based face detection, feature extraction methods applied to improve the performance of the system.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.45-56
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• 2020

Despite extensive research, performance enhancement of keyphrase (KP) extraction remains a challenging problem in modern informatics. Recently, deep learning-based supervised approaches have exhibited state-of-the-art accuracies with respect to this problem, and several of the previously proposed methods utilize Bidirectional Encoder Representations from Transformers (BERT)-based language models. However, few studies have investigated the effective application of BERT-based fine-tuning techniques to the problem of KP extraction. In this paper, we consider the aforementioned problem in the context of scientific articles by investigating the fine-tuning characteristics of two distinct BERT models - BERT (i.e., base BERT model by Google) and SciBERT (i.e., a BERT model trained on scientific text). Three different datasets (WWW, KDD, and Inspec) comprising data obtained from the computer science domain are used to compare the results obtained by fine-tuning BERT and SciBERT in terms of KP extraction.