• Journal of Digital Convergence
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• v.10 no.6
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• pp.297-302
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• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.171-181
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• 2001

The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.304-309
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• 2003

In this study, natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.62-70
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• 2007

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.154-156
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• 2003

인터넷의 급속한 성장과 더불어 많은 정보와 데이터들을 인터넷을 통하여 얻을 수 있게 되었으며 많은 단체들이 문서들을 웹을 통하여 이용 가능하게 만들고 있다. 이에 따라 다양한 정보와 데이터를 효과적으로 분류하고 검색하는 문서 분류 (Document Classification)에 대한 알고리즘이 다양한 분야에서 널리 연구되어 왔으며 본 논문에서 초점을 두고 있는 전자 도서관 (Digital Library) 분야에서도 활발히 연구되어지고 있다. 하지만 기존의 전자 도서관의 문서 분류 알고리즘들은 문서들의 각 단락의 비중을 고려하지 않은 채 단어들의 발생 빈도에 초점을 두어 많은 잡음 단어 (Noise Term)를 포함하고 그로 인하여 분류 성능이 떨어졌다. 본 논문에서는 문서 단락의 중요도에 따라 다른 .가중치를 부여하여 단어 지지도 (Term Support)가 높은 단어들을 추출하고 그 단어들로 연관 규칙 (Association Rules)을 이용하여 분류 규칙을 생성하는 방법을 제안한다. 제안된 방법의 성능평가를 위해 문서 분류에 널리 쓰이는 나이브 베이지안 분류자 (Na$\square$ve Bayesian Classifier) 및 기존의 단순 연관 규칙 분류자 (Associative Classifier)와 비교 평가하였다. 그 결과, 각 가중치가 부여된 연관 규칙 분류 방법이 나이브 베이지안 분류 방법과 단순 연관 규칙 분류 방법보다 높은 성능을 보였다.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Proceedings of the Korean Society for Information Management Conference
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• 2000.08a
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• pp.33-36
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• 2000

베이즈 확률을 이용한 분류기는 자동분류 초기부터 사용되어 아직까지 이 분야에서 가장 많이 사용되는 분류기 중 하나이다. 본 논문에서는 KTSET 문서에서 임의로 추출한 198건의 정보과학회 관련 논문의 제목 및 초록을 대상으로 베이즈 확률을 이용한 문서의 자동분류 실험을 수행하였으며, 더불어 Naive Bayesian 분류기에 가장 적합한 자질선정 방법을 찾고자 카이제곱 통계량, 상호정보량 및 기대상호정보량, 정보획득량, 역문헌빈도, 역카테고리빈도 등 6가지의 자질선정 기준을 실험하였다. 실험 결과는 카이제곱 통계량을 이용한 분류 실험의 성능이 가장 좋았고, 기대상호정보량과 정보획득량, 역카테고리빈도 또한 자질수에 큰 영향을 받지 않고 비교적 안정적인 성능을 보였다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.97-104
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• 2017

Location prediction has been successfully utilized to provide high quality of location-based services to customers in many applications. In its usual form, the conventional type of location prediction is to predict future locations based on user's past movement history. However, as location prediction needs are expanded into much complicated cases, it becomes necessary quite frequently to make inference on the locations that target user visited in the past. Typical cases include the identification of locations that infectious disease carriers may have visited before, and crime suspects may have dropped by on a certain day at a specific time-band. Therefore, primary goal of this study is to predict locations that users visited in the past. Information used for this purpose include user's demographic information and movement histories. Data mining classifiers such as Bayesian network, neural network, support vector machine, decision tree were adopted to analyze 6868 contextual dataset and compare classifiers' performance. Results show that general Bayesian network is the most robust classifier.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.945-952
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• 2007

The face detection is a necessary first-step in the face recognition systems, with the purpose of localizing and extracting face regions from input images. But it is not a simple problem, because faces have many variations such as scale, rotation and lighting condition. In this paper, we propose a novel method to detect not only frontal faces but also partial rotated faces in still images. Firstly, we produce the eye candidates in the sub-regions of an input image to detect rotated faces. Secondly, the eye candidates are used to measure the angles of rotated faces. Thirdly, we are able to derotate the rotated face then put it to Bayesian classifier. We make an experiment with rotated multiracial face and show the good results in this paper.