• Journal of Mechanical Science and Technology
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• 제19권2호
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• pp.558-566
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• 2005

We propose a feature-based multi-resolution representation of B-rep solid models using history-based Boolean operations based on the merge-and-select algorithm. Because union and subtraction are commutative in the history-based Boolean operations, the integrity of the models at various levels of detail (LOD) is guaranteed for the reordered features regardless of whether the features are subtractive or additive. The multi-resolution solid representation proposed in this paper includes a non-manifold topological merged-set model of all feature primitives as well as a feature-modeling tree reordered consistently with a given LOD criterion. As a result, a B-rep solid model for a given LOD can be provided quickly, because the boundary of the model is evaluated without any geometric calculation and extracted from the merged set by selecting the entities contributing to the LOD model shape.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권9호
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• pp.375-384
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• 2021

딥러닝 기법은 영상 처리 분야에서 높은 성능을 입증 받아 다양한 분야에서 적용되고 있다. 이러한 딥러닝 모델의 검증에 가장 널리 사용되는 방법으로는 홀드아웃 검증 방법, k-겹 교차 검증 방법, 부트스트랩 방법 등이 있다. 이러한 기존의 기법들은 데이터 셋을 분할하는 과정에서 클래스 간의 비율에 대한 균형을 고려하지만, 같은 클래스 내에서도 존재하는 다양한 특징들의 비율은 고려하지 않고 있다. 이러한 특징들을 고려하지 않을 경우, 일부 특징에 편향된 검증 결과를 얻게 될 수 있다. 따라서 본 논문에서는 기존 검증 방법들을 개선하여 영상 분류를 위한 데이터 특징 커버리지 기반의 딥러닝 모델 검증 기법을 제안한다. 제안하는 기법은 딥러닝 모델의 학습과 검증을 위한 훈련 데이터 셋과 평가 데이터 셋이 전체 데이터 셋의 특징을 얼마나 반영하고 있는지 수치로 측정할 수 있는 데이터 특징 커버리지를 제안한다. 이러한 방식은 전체 데이터 셋의 특징을 모두 포함하도록 커버리지를 보장하여 데이터 셋을 분할할 수 있고, 모델의 평가 결과를 생성한 특징 군집 단위로 분석할 수 있다. 검증결과, 훈련 데이터 셋의 데이터 특징 커버리지가 낮아질 경우, 모델이 특정 특징에 편향되게 학습하여 모델의 성능이 낮아지며, Fashion-MNIST의 경우 정확도가 8.9%까지 차이나는 것을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of Information Processing Systems
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• 제12권2호
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• pp.183-195
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• 2016

This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non-automotive vehicles or pedestrians, as these would interfere with the results.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• 한국지능시스템학회논문지
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• 제21권6호
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

• Genomics & Informatics
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• 제17권4호
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• 방송공학회논문지
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• 제9권1호
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• pp.2-7
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• 2004

본 논문에서는 내용 기반 음악 정보 검색에 MPEG-7에 정의된 오디오 서술자를 적용하는 방법을 제안한다. 특히 Pitch 정보와 timbral 특징들은 음색 구분을 용이하게 할 수 있어 음악 검색뿐만 아니라 음악 장르 분류 또는 QBH(Query By Humming)에 이용 될 수 있다. 이러한 방법을 통하여 오디오 신호의 대표적인 특성을 표현 할 수 있는 특징벡터를 구성 할 수 있다면 추후에 멀티모달 시스템을 이용한 검색 알고리즘에도 오디오 특징으로 이용 될 수 있을 것이다. 본 논문에서는 방송 시스템에 적용하기 위해 영화나 드라마의 배경음악에 해당하는 O.S.T 앨범으로 검색 범위를 제한하였다. 즉, 사용자가 임의로 검색을 요청한 시점에서 비디오 컨텐츠로부터 추출한 임의의 오디오 클립만을 이용하여 그 컨텐츠 전체의 O.S.T 앨범 내에서 음악을 검색할 수 있도록 하였다. 오디오 특징 백터를 구성하기 위해 필요한 MPEG-7 오디오 서술자의 조합 방법을 제안하고 distance 또는 ratio 계산 방식을 통해 성능 향상을 추구하였다. 또한 reference 음악의 템플릿 구성 방식의 변화를 통해 성능 향상을 추구하였다. Classifier로 k-NN 방식을 사용하여 성능평가를 수행한 결과 timbral spectral feature 보다는 pitch 정보를 이용한 특징이 우수한 성능을 보였고 vector distance 방식으로는 특징들의 비율을 이용한 IFCR(Intra-Feature Component Ratio) 방식이 ED(Euclidean Distance) 방식보다 우수한 성능을 보였다.

• 한국ITS학회 논문지
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• 제17권1호
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• pp.123-128
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• 2018

본 논문에서는 효율적인 차량 객체를 추적하는 특징점 매칭을 이용한 다중 차량 객체 검출 알고리즘을 제안한다. 제안하는 알고리즘은 효율적인 차량 객체 추적을 위해 FAST 알고리즘을 이용해서 차량의 특징점을 추출한다. 그리고 5X5 영역으로 분할 된 영상에서 특징점이 포함되면 True 포함되지 않으면 False로 해당 영역을 검은색으로 후처리하여 차량 객체을 제외한 불필요한 객체 정보를 제거한다. 그리고 후처리 된 영역을 차량의 최대 탐색창 크기로 설정하고, 차량의 최외각 특징점을 이용한 최소 탐색창을 설정하여 Mean-Shift 알고리즘의 탐색창 크기에 대한 단점을 보완하여 차량 객체 추적을 한다. 제안한 방법의 성능 평가하기위해 SIFT, SURF 알고리즘을 비교하여 실험한다. 그 결과 SIFT 알고리즘에 비해서 약 4배 빠르고 SUFR 알고리즘의 처리 과정 보다는 효율적으로 검출하는 장점이 있다.