Cheong Hee Park

Chungnam National University, Sŏngnam, Gyeonggi Province, South Korea

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Publications (24)4.8 Total impact

  • [show abstract] [hide abstract]
    ABSTRACT: In this paper, we propose a number recognition method to quickly and accurately get bus route information for the visually-impaired from natural scenes. We take a boosting approach based on Modified Census Transform (MCT) features, applied with success to number detection. Then, a gradient-based feature extraction algorithm is used to recognize the route number from detected samples. Finally, the recognition result is spoken by the Text-To-Speech (TTS) engine via the speaker embedded in tablet PC so that this information can be used when they take a bus without someone's help. In the experiment, we evaluate the performance of number detection and recognition in our dataset. We achieve high accuracy in both detection and recognition to provide reliable information of the bus routes.
    Ubiquitous Robots and Ambient Intelligence (URAI), 2013 10th International Conference on; 01/2013
  • Hoyoung Woo, Cheong Hee Park
    [show abstract] [hide abstract]
    ABSTRACT: Ensemble learning has been widely used in data mining and pattern recognition. However, when the number of labeled data samples is very small, it is difficult to train a base classifier for ensemble learning, therefore, it is necessary to utilize an abundance of unlabeled data effectively. In most semi-supervised ensemble methods, the label prediction of unlabeled data and their use as pseudo-label data are common processes. However, the low accuracy of the label prediction of unlabeled data limits the ability to obtain improved ensemble members. In this paper, we propose effective ensemble learning methods for semi-supervised classification that combine label propagation and ensemble learning. We show that accurate ensemble members can be constructed using class labels predicted by a label propagation method, and unlabeled data samples are fully utilized for diverse ensemble member construction. Extensive experimental results demonstrate the performance of the proposed methods.
    Computer and Information Technology (CIT), 2012 IEEE 12th International Conference on; 01/2012
  • Source
    Yong Joon Shin, Cheong Hee Park
    Applied Mathematics and Computer Science. 01/2011; 21:549-558.
  • Young Tae Lee, Yong Joon Shin, Cheong Hee Park
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    ABSTRACT: Linear discriminant analysis(LDA) is a traditional dimension reduction method which finds projective directions to maximize separability between classes. However, when the number of labeled data points is small, the performance of LDA is degraded severely. In this paper, we propose two improved methods for LDA which utilizes abundant unlabeled data. Instead of using all the unlabeled data as in most of semi- supervised dimension reduction methods, we select confident unlabeled data and develop extended LDA algorithms. In the first method, a graph-based LDA method is developed to utilize confidence scores for chosen unlabeled data so that unlabeled data with a low confidence score contributes smaller than unlabeled data with a high confidence score. In the second method, selected unlabeled data points are used to modify the centroids of classes in an objective function of LDA. Extensive experimental results in text classification demonstrates the effectiveness of the proposed methods compared with other semi-supervised dimension reduction methods.
    01/2011;
  • Cheong Hee Park, Hongsuk Shim
    IJPRAI. 01/2010; 24:1-14.
  • Cheong Hee Park, Moonhwi Lee
    [show abstract] [hide abstract]
    ABSTRACT: Associative classification has been recently proposed which combines association rule mining and classification, and many studies have shown that associative classifiers give high prediction accuracies compared with other traditional classifiers such as a decision tree. However, in order to apply association rule mining to classification problems, data transformation into the form of transaction data should be preceded before applying association rule mining. In this paper, we propose a discretization method based on Support vector machines, which can improve the performance of association classification greatly. The proposed method finds optimal class boundaries by using SVM, and discretization utilizing distances to the boundaries is performed. Experimental results demonstrate that performing SVM-based discretization for continuous attributes makes associative classification more effective in that it reduces the number of classification rules mined and also improves the prediction accuracies at the same time.
    Expert Syst. Appl. 01/2009; 36:4784-4787.
  • Gaksoo Lim, Cheong Hee Park
    Ninth IEEE International Conference on Computer and Information Technology, Xiamen, China, CIT 2009, 11-14 October 2009, Proceedings, Volume I; 01/2009
  • Cheong Hee Park
    [show abstract] [hide abstract]
    ABSTRACT: Dimension reduction is a preprocessing step by which small number of optimal features are extracted. Among several statistical dimension reduction methods, Linear discriminant analysis (LDA) performs dimension reduction to maximize class separability in the reduced dimensional space. However, in multi-labeled problems, data samples belonging to multiple classes cause contradiction between the maximization of the distances between classes and the minimization of the scatter within classes, since they are placed in the overlapping area of multiple classes. In this paper, we show that in multi-labeled text categorization, the outputs from multiple linear methods can be used to compose new features for low dimensional representation. Especially, we apply least squares regression and a linear support vector machine (SVM) for multiple binary-class problems constructed from a multi-labeled problem and obtain optimal features in a low dimensional space which are fed into another classification algorithm. Extensive experimental results in text categorization are presented comparing with other dimension reduction methods and multi-label classification algorithms.
    Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on; 08/2008
  • Cheong Hee Park, Moonhwi Lee
    [show abstract] [hide abstract]
    ABSTRACT: Associative classification has been recently proposed which combines association rule mining and classification, and many studies have shown that associative classifiers have high prediction accuracies. In order to apply an asso- ciation rule mining to classification problem, data transfor- mation into the form of transaction data should be preceded before applying association rule mining. In this paper, we propose a discretization method based on Support vector machines, which is very effective for association classifica- tion. The proposed method finds optimal class boundaries by using SVM, and discretization utilizing distances to the boundaries is performed. Experimental results demonstrate that performing SVM-based discretization for continuous attributes makes associative classification more effective in that it reduces the number of association rules mined and also improves the prediction accuracies at the same time.
    Computational Intelligence and Security, 2007 International Conference on; 01/2008
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    IADIS European Conference on Data Mining 2008, Amsterdam, The Netherlands, July 24-26, 2008. Proceedings; 01/2008
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    Cheong Hee Park, Moonhwi Lee
    [show abstract] [hide abstract]
    ABSTRACT: Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structure, LDA can achieve computational efficiency and also improve classification performances.
    Pattern Recognition Letters. 01/2008;
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    Cheong Hee Park, Haesun Park
    [show abstract] [hide abstract]
    ABSTRACT: Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make LDA applicable, several generalizations of LDA have been proposed recently. In this paper, we present theoretical and algorithmic relationships among several generalized LDA algorithms and compare their computational complexities and performances in text classification and face recognition. Towards a practical dimension reduction method for high dimensional data, an efficient algorithm is proposed, which reduces the computational complexity greatly while achieving competitive prediction accuracies. We also present nonlinear extensions of these LDA algorithms based on kernel methods. It is shown that a generalized eigenvalue problem can be formulated in the kernel-based feature space, and generalized LDA algorithms are applied to solve the generalized eigenvalue problem, resulting in nonlinear discriminant analysis. Performances of these linear and nonlinear discriminant analysis algorithms are compared extensively.
    Pattern Recognition. 01/2008;
  • Moonhwi Lee, Cheong Hee Park
    [show abstract] [hide abstract]
    ABSTRACT: While much progress has been made in face recognition over the last decades, changes in illumination directions still remain as a difficult problem. In this paper, we propose an efficient image normalization method which can overcome illumination effects effectively. The proposed method is based on intensity distribution transformation. However, instead of applying it globally, transformation in intensity distribution is performed for each column independently using one frontal mean face as a reference image. Since it does not require image processing such as image segmentation, the computational complexity is very low and it can circumvent boundary discontinuity caused by region segmentation. Extensive experimental results using Feret database and extended Yale B database demonstrate the competence of the proposed method.
    Proceedings of the 1st ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2008, Vancouver, British Columbia, Canada, October 30-31, 2008; 01/2008
  • Conference Proceeding: On Detecting an Emerging Class
    Cheong Hee Park, Hongsuk Shim
    [show abstract] [hide abstract]
    ABSTRACT: Most of classifiers implicitly assume that data samples belong to at least one class among predefined classes. How- ever, all data patterns may not be known at the time of data collection or a new pattern can be emerging over time. Hence ideal classifiers need to be able to recognize an emerging pattern. In this paper, we explore the per- formances and limitations of the existing classification sys- tems in detecting a new class. Also a new method is pro- posed that can monitor the change in class distribution and detect an emerging class. It works under the supervised learning model where along with classification an emerg- ing class with new characteristic is detected so that classi- fication model can be adapted systematically. For detection of an emerging class, we design statistical significance test- ing for signaling change of class distribution. When the alarm for new class generation is set on, candidates for new class members are retrieved for close examination by experts. Our experimental results demonstrate competent performances of the proposed method.
    Granular Computing, 2007. GRC 2007. IEEE International Conference on; 12/2007
  • Moonhwi Lee, Cheong Hee Park
    [show abstract] [hide abstract]
    ABSTRACT: Traditional classification problem assumes that a data sample belongs to one class among the predefined classes. On the other hand, in a multi-labeled problem such as text categorization, data samples can belong to multiple classes and the task is to output a set of class labels associated with new unseen data sample. As common in text categorization problem, learning a classifier in a high dimensional space can be difficult, known as the curse of dimensionality. It has been shown that performing dimension reduction as a preprocessing step can improve classification performances greatly. Especially, Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, which is optimized for classification tasks. However, in applying LDA for a multi-labeled problem some ambiguities and difficulties can arise. In this paper, we study on applying LDA for a multi-labeled problem and analyze how an objective function of LDA can be interpreted in multi-labeled setting. We also propose a LDA algorithm which is effective in a multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structures LDA can achieve computational efficiency and also improve classification performances greatly.
    Machine Learning and Data Mining in Pattern Recognition, 5th International Conference, MLDM 2007, Leipzig, Germany, July 18-20, 2007, Proceedings; 01/2007
  • Cheong Hee Park
    [show abstract] [hide abstract]
    ABSTRACT: Feature extraction is an important preprocessing step which is encountered in many areas such as data mining, pattern recognition and scientific visualization. In this paper, a new method for sparse feature extraction using local manifold learning is proposed. Similarities in a neighborhood are first computed to explore local geometric structures, producing sparse feature representation. Based on the constructed similarity matrix, linear dimension reduction is applied to enhance similarities among the elements in the same class and extract optimal features for classification performances. Since it only computes similarities in a neighborhood, sparsity in the similarity matrix can give computational efficiency and memory savings. Experimental results demonstrate superior performances of the proposed method.
    Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings; 01/2006
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    Cheong Hee Park, Haesun Park
    [show abstract] [hide abstract]
    ABSTRACT: Linear Discriminant Analysis (LDA) has been widely used for linear dimension reduction. However, LDA has some limitations that one of the scatter matrices is required to be nonsingular and the nonlinearly clustered structure is not easily captured. In order to overcome the problems caused by the singularity of the scatter matrices, a generalization of LDA based on the generalized singular value decomposition (GSVD) has been developed recently. In this paper, we propose a nonlinear discriminant analysis based on the kernel method and the generalized singular value decomposition. The GSVD is applied to solve the generalized eigenvalue problem which is formulated in the feature space defined by a nonlinear mapping through kernel functions. Our GSVD-based kernel discriminant anal- ysis is theoretically compared with other kernel-based nonlinear discriminant analysis algorithms. The experimental results show that our method is an effective nonlinear dimension reduction method.
    SIAM J. Matrix Analysis Applications. 01/2005; 27:87-102.
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    Cheong Hee Park, Haesun Park
    SIAM J. Matrix Analysis Applications. 01/2005; 27:474-492.
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    Cheong Hee Park, Haesun Park
    [show abstract] [hide abstract]
    ABSTRACT: In this paper, we present a new approach for fingerprint classification based on discrete Fourier transform (DFT) and nonlinear discriminant analysis. Utilizing the DFT and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low-quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.
    Pattern Recognition. 01/2005;
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    Cheong Hee Park, Haesun Park, P. Pardalos
    [show abstract] [hide abstract]
    ABSTRACT: This paper presents theoretical relationships among several generalized LDA algorithms and proposes computationally efficient approaches for them utilizing the relationships. Generalized LDA algorithms are extended nonlinearly by kernel methods resulting in nonlinear discriminant analysis. Performances and computational complexities of these linear and nonlinear discriminant analysis algorithms are compared.
    Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on; 12/2004

Publication Stats

157 Citations
33 Downloads
1k Views
4.80 Total Impact Points

Institutions

  • 2008–2009
    • Chungnam National University
      • Department of Computer Science and Engineering
      Sŏngnam, Gyeonggi Province, South Korea
    • Georgia Institute of Technology
      • College of Computing
      Atlanta, GA, United States
  • 2004–2005
    • University of Minnesota Twin Cities
      • Department of Computer Science and Engineering
      Minneapolis, MN, United States
    • University of Minnesota Duluth
      • Department of Computer Science
      Duluth, MN, United States