• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권1호
• /
• pp.60-79
• /
• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제6권10호
• /
• pp.2632-2649
• /
• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• 한국한의학연구원논문집
• /
• 제12권3호통권18호
• /
• pp.1-15
• /
• 2006

최근 수년간 한국어를 위한 어휘의미망에 대한 관심은 꾸준히 높아지고 있지만, 그 결과물을 어떻게 평가하고 활용할 것인가에 대한 방안은 이루어지지 않고 있다. 본 논문에서는 단어클러스터링 시스템 개발을 통하여, 어휘의미망에 의해 확장되기 전후의 클러스터링을 수행하여 데이터를 서로 비교하였다. 단어클러스터링 시스템 개발을 위해 사용된 학습 데이터는 신문 말뭉치 기사로 총 68,455,856 어절 규모이며, 특성벡터와 벡터공간모델을 이용하여 시스템A를 완성하였다. 시스템B는 구축된 '[-하]동사류' 3,656개의 어휘의미를 포함하는 동사 어휘의미망을 활용하여 확장된 것으로 확장대상정보를 선택하여 특성벡터를 재구성한다. 대상이 되는 실험 데이터는 '다국어 어휘의미망-코어넷'으로 클러스터링 결과 나타난 어휘의 세 번째 층위까지의 노드 동일성 여부로 정확률을 검수하였다. 같은 환경에서 시스템A와 시스템B를 비교한 결과 단어클러스터링의 정확률이 45.3%에서 46.6%로의 향상을 보였다. 향후 연구는 어휘의미망을 활용하여 좀 더 다양한 시스템에 체계적이고 폭넓은 평가를 통해 전산시스템의 향상은 물론, 연구되고 있는 많은 어휘의미망에 의미 있는 평가 방안을 확대시켜 나가야 할 것이다.

• 대한음성학회지:말소리
• /
• 제57호
• /
• pp.59-72
• /
• 2006

The purpose of this study was to see whether Korean-English bilinguals' semantic systems of Korean and English are shared or separated between the two languages. In a series of picture-word interference tasks, participants were required to name the pictures in Korean or in English with distractor words printed either in Korean or English. The distractor words were any of identical, semantically related, or neutral to the picture. The response time of naming was facilitated when distractor words were semantically identical for both same- and different-language pairs. But this facilitation effect was stronger when naming was produced in their native language, which in this case was Korean. Also, inhibitory effect was found when the picture and its distractor word were semantically related in both same- and different-language paired conditions. From these results it can be concluded that semantic representations of Korean and English may not be entirely but partly overlapping in bilinguals.

• 한국정보과학회논문지:데이타베이스
• /
• 제30권3호
• /
• pp.237-251
• /
• 2003

이 논문에서는 비디오 데이타의 논리적 구조와 의미적 내용을 효과적으로 검색하기 위한 비디오 검색 시스템을 제안한다. 제안하는 검색 시스템은 비정형화된 비디오 데이타를 원시 데이타 계층, 내용 계층 그리고 키프레임 계층의 세 계층으로 구성하는 계층화된 모델링을 사용한다. 계층화된 모델링에 존재하는 내용 계층은 비디오 데이타에 대한 논리적인 계층 구조와 의미적 내용을 표현한다. 제안하는 검색 시스템은 모델링에 따라 텍스트 기반의 검색은 물론 시각적인 특징 기반의 유사도 검색을 지원한다. 또한 시공간 관계에 기반한 의미적 내용 검색과 유사도 검색을 지원한다.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (1)
• /
• pp.283-285
• /
• 1998

현재의 멀티데이터베이스 시스템에서 고려해야 할 중요한 문제중의 하나는 의미 이질성(semantic heterogeneity)을 식별하고 해결하는 것이다. 본 논문에서는 이를 위하여, 다중 측면 의미 모델(Multi-Aspect Semantic Model:MASM)을 제시하고 이에 기반한 의미 통합 방법을 제시한다. MASM은 의미 특징(semantic feature), 스키마 측면(schematic aspect), 명칭(name), 기능적 측면(functional aspect), 문맥(context) 등의 여러 요소들을 고려한 모델이며, 모든 요소 데이터베이스간에 공유되어야 하는 표준화된 지식 없이 객체간의 의미 유사성을 판단한다. 정보 통합에 필요한 모든 지식은 각 요소 데이터베이스에서 다른 요소 데이터베이스에 독립적으로 구축되며, 이를 통하여 융통성과 확장성을 갖는 멀티데이터베이스 시스템을 구축하는 토대를 마련한다.

• 제어로봇시스템학회논문지
• /
• 제17권3호
• /
• pp.277-282
• /
• 2011

This paper describes a sensor fusion-based semantic map building which can improve the capabilities of a mobile robot in various domains including localization, path-planning and mapping. To build a semantic map, various environmental information, such as doors and cliff areas, should be extracted autonomously. Therefore, we propose a method to detect doors, cliff areas and robust visual features using a laser scanner and a vision sensor. The GHT (General Hough Transform) based recognition of door handles and the geometrical features of a door are used to detect doors. To detect the cliff area and robust visual features, the tilting laser scanner and SIFT features are used, respectively. The proposed method was verified by various experiments and showed that the robot could build a semantic map autonomously in various indoor environments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권10호
• /
• pp.3972-3988
• /
• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• 한국정보통신학회논문지
• /
• 제15권7호
• /
• pp.1517-1524
• /
• 2011

본 논문은 의미특징과 워드넷 기반의 의사연관피드백을 이용하여 사용자의 질의에 관련 있는 의미 있는 문장을 추출하여 문서요약을 하는 새로운 방법을 제안한다. 제안된 방법은 비음수 행렬 분해로부터 유도된 의미특정이 문서의 잠재의미를 잘 나타나기 때문에 문서요약의 질을 향상할 수 있다. 또한 의미특정과 워드넷기반의 의사연관피드백을 이용하여서 사용자의 요구사항과 제안방법의 요약결과 사이의 의미적 차이를 감소시킨다. 실험결과 제안방법이 유사도, 비음수행렬분해를 이용한 방법들에 비하여 좋은 성능을 보인다.

• ETRI Journal
• /
• 제33권5호
• /
• pp.720-730
• /
• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.