• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1111-1113
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• 2003

In this paper, we investigated a method for feature - based classification to develop software which is suitable to the classification of high resolution satellite imagery . So, we developed related algorithm and designed user interfaces of convenience, considering various elements require for the feature - based classification. The software was tested with eCognition software which is unique commercial software for feature - based classification.

• 대한공간정보학회지
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• 제12권2호
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• pp.53-59
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• 2004

본 연구에서는 고해상도 위성영상의 분류에 적합한 형상 기반 분류 소프트웨어를 개발하기 위한 연구를 수행하였다. 형상 기반 분류에 필요한 영상분할과 퍼지 기반의 분류 알고리즘을 개발하고, 형상 기반 분류에 요구되는 다양한 요소들을 고려하여 사용자와의 원활한 상호작용을 지원하기 위한 사용자 인터페이스를 구현하였다. 개발된 소프트웨어의 성능을 평가하고자 본 연구에서 개발된 소프트웨어와 현재 전 세계적으로 널리 보급되고 있는 형상 기반 분류 관련 상용 소프트웨어인 eCognition을 적용하여 동일한 영상을 시험적으로 처리해 본 결과 유사한 영상 분류결과를 얻을 수 있었다. 영상분할의 경우에는 본 연구에서 개발한 소프트웨어의 처리속도가 우수하였다. 형상 기반 분류를 수행하는 데에는 프로그램과 사용자간의 고도의 상호작용이 요구되므로, 향후에 이를 편리하게 하기 위한 사용자 인터페이스의 보완이 필요하다는 것을 알 수 있었다.

• 대한공간정보학회지
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• 제19권2호
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• pp.9-17
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• 2011

국내 여러 기관에서 토지피복분류체계, 토지이용현황분류체계 등 국토의 정확한 현황 파악을 위해 다양한 지형분류체계를 활용 중에 있다. 그러나 이러한 분류체계로 국토변화를 탐지하기에는 적용성이 떨어지며, 변화지역을 추출하기에도 적합하지 않다는 문제점을 가지고 있다. 본 연구에서는 국토에 대한 자연적, 인위적 변화요소들을 모두 효과적으로 나타낼 수 있는 표준 지형분류체계를 제안하고자 한다. 이를 위해 국내외 유사 지형분류체계에 대한 비교 분석을 수행하고, 이를 바탕으로 표준 지형분류 항목을 제안하였다. 자동 지형분류 적용 가능성을 평가하기 위하여 감독분류 기반의 자동 지형분류와 선행지식 기반의 자동 지형분류를 수행하여 정확도를 평가하였다.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• The Journal of the Acoustical Society of Korea
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• 제23권3E호
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• pp.91-97
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• 2004

In this paper, we propose a classification system of TV program scenes based on audio information. The system classifies the video scene into six categories of commercials, basketball games, football games, news reports, weather forecasts and music videos. Two type of audio feature set are extracted from each audio frame-timbral features and coefficient domain features which result in 58-dimensional feature vector. In order to reduce the computational complexity of the system, 58-dimensional feature set is further optimized to yield l0-dimensional features through Sequential Forward Selection (SFS) method. This down-sized feature set is finally used to train and classify the given TV program scenes using κ -NN, Gaussian pattern matching algorithm. The classification result of 91.6% reported here shows the promising performance of the video scene classification based on the audio information. Finally, the system stability problem corresponding to different query length is investigated.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.421-427
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• 2001

In this paper, a method for selection of the optimal feature vectors is proposed for the classification of closed 2D shapes using the bispectrum of a contour sequence. The bispectrum based on third order cumulants is applied to the contour sequences of the images to extract feature vectors for each planar image. These bispectral feature vectors, which are invariant to shape translation, rotation and scale transformation, can be used to represent two-dimensional planar images, but there is no certain criterion on the selection of the feature vectors for optimal classification of closed 2D images. In this paper, a new method for selecting the optimal bispectral feature vectors based on the variances of the feature vectors. The experimental results are presented using eight different shapes of aircraft images, the feature vectors of the bispectrum from five to fifteen and an weighted mean fuzzy classifier.

• Journal of information and communication convergence engineering
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• 제5권3호
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• pp.245-248
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• 2007

In this paper, we research the content-based analysis and classification according to the composition of the feature parameter data base for the audio data to implement the audio data index and searching system. Audio data is classified to the primitive various auditory types. We described the analysis and feature extraction method for the feature parameters available to the audio data classification. And we compose the feature parameters data base in the index group unit, then compare and analyze the audio data centering the including level around and index criterion into the audio categories. Based on this result, we compose feature vectors of audio data according to the classification categories, and simulate to classify using discrimination function.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 대한의용생체공학회:의공학회지
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• 제42권6호
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• ETRI Journal
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• 제33권2호
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• pp.210-218
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• 2011

Battiti's mutual information feature selector (MIFS) and its variant algorithms are used for many classification applications. Since they ignore feature synergy, MIFS and its variants may cause a big bias when features are combined to cooperate together. Besides, MIFS and its variants estimate feature redundancy regardless of the corresponding classification task. In this paper, we propose an automated greedy feature selection algorithm called conditional mutual information-based feature selection (CMIFS). Based on the link between interaction information and conditional mutual information, CMIFS takes account of both redundancy and synergy interactions of features and identifies discriminative features. In addition, CMIFS combines feature redundancy evaluation with classification tasks. It can decrease the probability of mistaking important features as redundant features in searching process. The experimental results show that CMIFS can achieve higher best-classification-accuracy than MIFS and its variants, with the same or less (nearly 50%) number of features.