• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.965-974
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• 2007

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1857_1858
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• 2009

본 논문에서는 PCA와 입자 군집 최적화 알고리즘을 이용한 얼굴이미지에서 특징선택 방법에 대하여 제안한다. 2차원 얼굴이미지의 히스토그램 분표값에서 정규화합 연산을 이용한 히스토그램 평활화 기법을 거쳐 대비효과를 주어 화질을 개선시켜 준다. PCA는 2차원 얼굴이미지를 이용하여 공분산 행렬을 구한 후 그것의 고유값에 따른 고유벡터를 구하여 얼굴인식에 사용될 특징 벡터들을 추출한다. 또한 추출된 특징벡터 중에서 얼굴인식 성능에 중요한 요소가 되는 특징 벡터들을 입자 군집 최적화 알고리즘을 이용하여 최적화한다. 다항식 기반 RBF 신경회로망을 사용하여 얼굴인식 성능을 평가한다. 본 논문에서 제안된 방법을 통해 최적화된 특징벡터와 얼굴인식률과의 관계를 알 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.577-579
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• 2003

본 논문에서는 생체인식 분야 중 얼굴인식의 검색 정확성 향상 및 검색 시간을 단축하기 위한 단계로 인종별 얼굴영상 데이터베이스에 대한 군집화 기법을 연구하였다. 우선, 일반적으로 얼굴 및 이미지 검색에 사용되는 다양한 특징을 추출하고, 추출한 다차원의 특징 데이터들로부터 다 인종 얼굴 데이터를 유사한 인종별로 정확하게 군집화 하기 위해 최적의 특징벡터를 자동으로 선택 할 수 있는 방법을 제안하였다. 군집결과 분석을 위해 자기 조직화 지도 모형을 이용하였는데, 이는 2차원 분석 및 가시화에 유용하며, 학습 후 코드북벡터를 사용하여 유사한 의미간의 거리부터 검색할 수 있는 특징을 가지고 있다. 특징추출에 관한 실험결과 인종별 구분을 위한 특징벡터로는 웨이블릿 주파수 성분(lowpass 성분)과 CbCr 특징벡터가 인종별 군집화에 가장 유용한 특징으로 선택되었으며. 추출된 특징을 바탕으로 semantic map을 구성하여 제안방법의 효율성을 제시하였다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.543-551
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• 1999

The primary purposes in this paper are to analyze the class separation of features in handwritten numeral recognition and to make use of the results in feature selection. Using the Parzen window technique, we compute the class distributions and define the class separation to be the overlapping distance of two class distributions. The dimension of a feature vector is reduced by removing the void or redundant feature cells based on the class separation information. The experiments have been performed on the CENPARMI handwritten numeral database, and partial classification and full classification have been tested. The results show that the class separation is very effective for the feature selection in the 10-class handwritten numeral recognition problem since we could reduce the dimension of the original 256-dimensional feature vector by 22%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.67-76
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• 2008

Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.119-125
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• 2021

As the electricity grid systems has been intelligent with the development of ICT technology, power consumption information of users connected to the grid is available to acquired and analyzed for the power utilities. In this paper, the energy theft problem is solved by feature selection methods, which is emerging as the main cause of economic loss in smart grid. The data preprocessing steps of the proposed system consists of five steps. In the feature selection step, features are selected using analysis of variance and mutual information (MI) based method, which are filtering-based feature selection methods. According to the simulation results, the performance of support vector machine classifier is higher than the case of using all the input features of the input data for the case of the MI based feature selection method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.617-618
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• 2017

음성인식의 적절한 성능을 구하기 위하여 잡음환경 하에서 최적인 음성의 특징 벡터를 선택할 필요가 있다. 본 논문에서는 위너필터 방법과 인간의 청각계의 특성을 활용한 멜 주파수 켑스트럼 계수를 사용한 음성인식 방법을 제안한다. 본 논문에서 제안하는 음성의 특징 벡터는 음성 중에서 배경잡음을 제거한 후에 깨끗한 음성신호의 벡터를 추출하는 방법이며, 다층 퍼셉트론 신경회로망에 멜 주파수 켑스트럼 계수를 입력하여 학습시킴으로써 음성인식을 구현한다. 본 실험에서는 멜 주파수 켑스트럼 계수의 특징 벡터를 사용하여 백색잡음이 혼합된 경우에 대하여 음성인식 실험을 실시하였다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1391-1392
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• 2015

최근 컴퓨터 기술이 발전하고, 컴퓨터의 형태가 다양해지면서 여러 wearable device들이 생겨났다. 이에 따라 휴먼 인터페이스 기술에서 사람의 감정정보가 중요해졌고, 감정인식에 대한 연구들이 많이 진행 되어 왔다. 본 논문에서는 감정분석에 적합한 특징벡터를 제시하고자 한다. 이를 위해 사람의 감정을 보통, 기쁨, 슬픔, 화남 4가지로 분류하고 방송매체를 통하여 잡음 없이 녹음하였다. 특징벡터는 MFCC, LPC, LPCC 3가지를 추출하였고 Bhattacharyya거리 측정을 통하여 분리도를 비교하였다.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.17-25
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• 2009

This paper suggests fuzzy-based Segment-Boost method and an effective method for face recognition using the fuzzy-based Segment-Boost. Fuzzy-based Segment-Boost eliminates the limitations of Segment-Boost, and it guarantees improved learning performance and the stability of the performance. By using the fuzzy theory, fuzzy-based Segment-Boost optimizes the selection number of sub-vectors, and leads the optimized learning performance. The fuzzy controller designed in this paper measures learning performance of the fuzzy-based Segment-Boost, and it controls the selection number of sub-vectors by inferring the optimized selection number. The simulation results show that the fuzzy controller inferred the selection number which is very approximate to the true optimized value. As a result, fuzzy-based Segment-Boost showed higher face recognition rate than compared boosting methods and it preserves the velocity of feature selection as fast as that of Segment-Boost. From the experimental results, it was proved that fuzzy-based Segment-Boost has improved and stable performances of learning, feature selection and face recognition.