• Journal of Information Processing Systems
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• v.3 no.1
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• pp.33-37
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• 2007

Semantic clustering is important to various fields in the modem information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a hierarchical clustering in latent semantic space for finding out invisible information from the data.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.249-257
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• 2017

This paper proposes a method to segment urban scenes semantically based on location prior information. Since major scene elements in urban environments such as roads, buildings, and vehicles are often located at specific locations, using the location prior information of these elements can improve the segmentation performance. The location priors are defined in special 2D coordinates, referred to as road-normal coordinates, which are perpendicular to the orientation of the road. With the help of depth information to each element, all the possible pixels in the image are projected into these coordinates and the learned prior information is applied to those pixels. The proposed location prior can be modeled by defining a unary potential of a conditional random field (CRF) as a sum of two sub-potentials: an appearance feature-based potential and a location potential. The proposed method was validated using publicly available KITTI dataset, which has urban images and corresponding 3D depth measurements.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.240-243
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• 2000

An apparel products retrieval system was proposed in which users can refer to products using Kansei evaluation values. The system adopts relevance feedback using history of the retrieval to learn the tendency of user evaluation. The system is based on a vector space retrieval model using products images expression as semantic scales. The system makes a query from user inputting information and retrieves closest products from the database. Revising algorithms of the difference method. linear multiple regression performed to investigate the effectiveness and criteria of the search. As a result of evaluation of the accuracy, it was found that the linear multiple regression and the neural network models are effective for the retrieval considering the individual Kansei.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.3
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• pp.150-170
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• 2008

In recent years, main focus of research on image retrieval techniques is on content-based image retrieval. Text-based image retrieval schemes, on the other hand, provide semantic support and efficient retrieval of matching images. In this paper, based on Formal Concept Analysis (FCA), we propose a new image indexing and retrieval technique. The proposed scheme uses keywords and textual annotations and provides semantic support with fast retrieval of images. Retrieval efficiency in this scheme is independent of the number of images in the database and depends only on the number of attributes. This scheme provides dynamic support for addition of new images in the database and can be adopted to find images with any number of matching attributes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.704-706
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• 2012

In this paper, we proposes a document summarization method using the term weighting. The proposed method can minimize the user intervention to use the pseudo relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.394-396
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• 2006

기존 질의기반의 문서요약은 질의와 문서간의 사전 학습으로 요약의 질을 높이거나, 문서의 고유 구조(inherent structure)를 반영하여 요약의 정확도를 높이기 위하여 문서를 그래프로 변환한다. 본 논문은 비음수 행렬 인수분해 (NMF, Non-negative Matrix Factorization)를 이용하여 질의 기반의 문서를 요약하는 새로운 방법을 제안하였다. 제안된 방법은 질의와 문서간에 사전학습이 필요 없다. 또한 문서를 그래프로 변형시키는 복잡한 처리 없이 NMF에 의해 얻어진 의미 특징(semantic feature)과 의미 변수(semantic variable)로 문서의 고유 구조를 반영하여 요약의 정확도를 높일 수 있다. 마지막으로 단순한 방법으로 문장을 쉽게 요약 할 수 있다.

• Language and Information
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• v.11 no.2
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• pp.67-86
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• 2007

The semantic literature on definiteness and specificity shows that the former is relatively an established notion, whereas the latter is still a vague notion that needs to be more clarified and confirmed. Given this, Ionin (2006) argues for the reality of specificity based on the informal use of this. She proposes this in spoken English as a specificity marker which has a semantic feature indicating "peaker intent to refer" and "noteworthiness." She also provides as evidence the results of some crosslinguistics studies including an L2 acquisition study with both L1-Russian and L1-Korean L2-English learners. However, this paper will argue that the informal use of this does not seem to mark specificity according to Ionin's definition of specificity. It will also be argued that the L2 acquisition study cannot be used as evidence for the reality of specificity. Based on these arguments, this paper will try to redefine specificity, based on the notions of existence and uniqueness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.305-306
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• 2013

In this paper, we proposes a document summarization method using the weighting based on cloud. The proposed method can minimize the user intervention to use the relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature using nonnegative matrix factorizaitno based cloud.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.968-969
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• 2013

In this paper, we proposes a document summarization method using the weighting based on cloud. The proposed method can minimize the user intervention to use the relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature using nonnegative matrix factorizaitno based cloud.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.