Cheong Hee Park

Chungnam National University, Daiden, Daejeon, South Korea

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Publications (39)28.8 Total impact

  • Cheong Hee Park
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    ABSTRACT: Query by humming (QBH) is to retrieve songs in the music database by using user׳s humming. In QBH, the huge size of a song database requires an efficient search method. Recently, local sensitive hashing (LSH) has been applied in QBH and showed its superior performance. In this paper, we propose a method for QBH which uses multiple spectral hashing (MSH) and scaled open-end dynamic time warping (SOEDTW). We construct multiple binary embedding spaces by utilizing eigenvectors obtained from spectral hashing, so we call this approach as multiple spectral hashing (MSH). We also apply an improved OEDTW method for similarity matching. The experimental results demonstrate that the proposed method can improve retrieval performance greatly.
    Signal Processing 03/2015; 108:220–225. DOI:10.1016/j.sigpro.2014.09.024 · 2.24 Impact Factor
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    ABSTRACT: The main cause of traffic accidents is drivers' human errors such as cognitive, judgment, and execution errors. To mitigate drivers' human errors, research on the measurement and quantification of driver workload as well as the development of smart vehicles is needed. Drivers' behavior while driving includes driving straight, turning left or right, U-turns, rapid acceleration, rapid deceleration, and changing lanes. To measure and quantify a driving workload, both the subjective workload and the behavior workload caused by varied driving behaviors should be taken into account on the basis of understanding the visual, auditory, cognitive, and psychomotor characteristics of the driving workload. In this paper, we analyze electroencephalogram (EEG) data collected through an urban road driving test. To overcome large deviations of EEG values among drivers, we used EEG variation rates instead of raw EEG values. We extracted five kinds of behavior sections from the data: left-turn section, right-turn section, rapid-acceleration section, rapid-deceleration section, and lane-change section. We then selected a reference section for each of these behavior sections and compared EEG values from the behavior sections with those from the reference sections to calculate the EEG variation rates, after which we made the statistical analysis. The analysis results of this study are being used to explain the cognitive characteristics of a driving workload caused by drivers' behavior in the vehicle information system, which will provide information for safe driving by taking into account the driving workload.
    IEEE Transactions on Intelligent Transportation Systems 08/2014; 15(4):1844-1849. DOI:10.1109/TITS.2014.2333750 · 2.47 Impact Factor
  • Cheong Hee Park
    06/2014; 3(6):215-218. DOI:10.3745/KTSDE.2014.3.6.215
  • Etri Journal 04/2014; 36(2):232-241. DOI:10.4218/etrij.14.2113.0086 · 0.95 Impact Factor
  • Yunji Kim, Cheong Hee Park
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    ABSTRACT: Query by Singing/Humming(QBSH) is to retrieve songs in the music database using user's singing or humming. Open-End Dynamic Time Warping (OEDTW) is one of the methods which are commonly used in QBSH studies. This paper proposes a method for improving OEDTW performance using optimal scaling factor taken from Linear scaling phase. The proposed method finds an optimal global scale of a query for each target song by linear scaling and key shifting of the query and target song over the obtained scale can complement DTW or OEDTW. The experimental results demonstrate that the proposed method can improve Top-1 hit rate by 32% compared with the original DTW.
    Proceedings of the 2013 International Conference on Signal-Image Technology & Internet-Based Systems; 12/2013
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    ABSTRACT: When any distracted driving of the driver is concerned, use of smartphones and navigation systems while driving are major causes for traffic accidents. However, in a situation where navigation systems are commonly installed and smartphones are commonly used within vehicles, it is better to implement technologies that control the use of smartphones in high-risk situations and allow brief periods of use otherwise, rather than ban the use of smartphones altogether while driving. This study introduces the DWMS (Driver Workload Management System) and proposes service that controls the use of smartphones according to driver's workloads. The DWMS periodically monitors the driver's behaviors and smartphone usages in order to calculate driving workload. When the calculated workload exceeds the threshold value, the driver is deemed to be overburdened and all incoming calls and SMS (Short Message Service) is blocked. The service will then make a notification when this status is relieved. In addition, at the end of driving, the DWMS will provide a summary of the information on how the driver drives, workload, and behaviors taken as a text message to the driver's smartphone. The result of this study can increase both driving safety and convenience for drivers.
    2013 IEEE Vehicular Networking Conference (VNC); 12/2013
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    ABSTRACT: We present optical fiber based large-scale color holographic three-dimensional display using a pupil tracking technique for wide viewing angle display applications. One of the limitations in implementing a large-scale holographic three-dimensional display is the restrictive space-bandwidth product of the spatial light modulator. In our proposed method, motor control system for steering optical fibers as a light guide and pupil tracking system are used to overcome the limitation of space-bandwidth product by steering the view window according to a position of the pupil of the observer. The proposed method employing the holographic stereogram in order to deliver two holographic patterns of different perspectives of a 3D scene to both eyes of the observer, which successfully provides both of the accommodation and binocular disparity.
    2013 International Conference on 3D Imaging (IC3D); 12/2013
  • Cheong Hee Park
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    ABSTRACT: In semi-supervised learning, when the number of data samples with class label information is very small, information from unlabeled data is utilized in the learning process. Many semi-supervised learning methods have been presented and have exhibited competent performance. Active learning also aims to overcome the shortage of labeled data by obtaining class labels for some selected unlabeled data from experts. However, the selection process for the most informative unlabeled data samples can be demanding when the search is performed over a large set of unlabeled data. In this paper, we propose a method for batch mode active learning in graph-based semi-supervised learning. Instead of acquiring class label information of one unlabeled data sample at a time, we obtain information about several data samples at once, reducing time complexity while preserving the beneficial effects of active learning. Experimental results demonstrate the improved performance of the proposed method.
    International Journal of Pattern Recognition and Artificial Intelligence 11/2013; 27(07). DOI:10.1142/S0218001413510026 · 0.56 Impact Factor
  • Hoyoung Woo, Cheong Hee Park
    10/2013; 2(10):705-712. DOI:10.3745/KTSDE.2013.2.10.705
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    ABSTRACT: In this paper, we measured and analyzed the driver's EEG for lane change behavior to measure and quantify drivers' workload. The experiment took place in the real road. We extracted the reference and the lane change behavior section and calculated the EEG variation rates from the EEG values. We performed paired-sample T-test to know EEG variation rate of reference and lane change section. Also, we performed ANOVA analysis to know the difference of EEG variation rate dependent on driving road type. We performed independent samples T-test to know the difference of EEG variation rate dependent on gender types.
    ICT Convergence (ICTC), 2013 International Conference on; 01/2013
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    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
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    ABSTRACT: We propose a user pose verification technique using depth information to compare their pose with expert riders. Xtion sensor by Asus is used for gathering a depth data in real world. The user pose verification algorithm is divided into two categories: user segmentation and user pose verification. In user segmentation step, body parts are segmented based on the region growing algorithm from a head point (i.e. seed point). Then, a simple algorithm is used to generate skeletal joints in the segmented body parts. Finally, the user pose is investigated by the standard pose of experts in the same situation.
    RO-MAN, 2013 IEEE; 01/2013
  • Hoyoung Woo, Cheong Hee Park
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    ABSTRACT: Active learning aims to select data samples which would be the most informative to improve classification performance so that their class labels are obtained from an expert. Recently, an active learning method based on locally linear reconstruction(LLR) has been proposed and the performance of LLR was demonstrated well in the experiments comparing with other active learning methods. However, the time complexity of LLR is very high due to matrix operations required repeatedly for data selection. In this paper, we propose an efficient active learning method based on random sampling and backward deletion. We select a small subset of data samples by random sampling from the total data set, and a process of deleting the most redundant points in the subset is performed iteratively by searching for a pair of data samples having the smallest distance. The distance measure using a graph-based shortest path distance is utilized in order to consider the underlying data distribution. Experimental results demonstrate that the proposed method has very low time complexity, but the prediction power of data samples selected by our method outperforms that by LLR.
    Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering; 10/2012
  • Hoyoung Woo, Cheong Hee Park
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    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
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    Yong Joon Shin, Cheong Hee Park
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    ABSTRACT: Dimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.
    International Journal of Applied Mathematics and Computer Science 01/2011; 21:549-558. DOI:10.2478/v10006-011-0043-9 · 1.39 Impact Factor
  • 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.
  • Cheong Hee Park
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    ABSTRACT: Dimension reduction has been applied in various areas of pattern recognition and data mining. While a traditional dimension reduction method, Principal Component Analysis (PCA) finds projective directions to maximize the global scatter in data, Locality Preserving Projection (LPP) pursues linear dimension reduction to minimize the local scatter. However, the discriminative power by either global or local scatter optimization is not guaranteed to be effective for classification. A recently proposed method, Unsupervised Discriminant Projection (UDP) aims to minimize the local scatter among near points and maximize the global scatter of distant points at the same time. Although its performance has been proven to be comparable to other dimension reduction methods, PCA preprocessing step due to the singularity of global and local scatter matrices may degrade the performance of UDP. In this paper, we propose several algorithms to improve the performances of UDP greatly. An improved algorithm for UDP is presented which applies the Generalized Singular Value Decomposition (GSVD) to overcome singularities of scatter matrices in UDP. Two-dimensional UDP and nonlinear extension of UDP are also proposed. Extensive experimental results demonstrate superiority of the proposed algorithms.
    International Journal of Pattern Recognition and Artificial Intelligence 03/2010; 24(2):193-206. DOI:10.1142/S0218001410007920 · 0.56 Impact Factor
  • Cheong Hee Park, Hongsuk Shim
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    ABSTRACT: Most traditional classifiers implicitly assume that data samples belong to at least one class among predefined several classes. However, all data patterns may not be known at the time of data collection or a new pattern can be emerging over time. In this paper, a new method is presented for monitoring the change in class distribution and detecting an emerging class. First a statistical significance test is designed which can signal for a change in class distribution. When an alarm for new class generation is set on, retrieval of new class members is performed using density estimation and entropy-based thresholding. Our experimental results demonstrate competent performances of the proposed method.
    International Journal of Pattern Recognition and Artificial Intelligence 02/2010; 24(1):1-14. DOI:10.1142/S0218001410007828 · 0.56 Impact Factor
  • Cheong Hee Park, Moonhwi Lee
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    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 Systems with Applications 04/2009; 36(3):4784-4787. DOI:10.1016/j.eswa.2008.06.033 · 1.97 Impact Factor
  • Gaksoo Lim, Cheong Hee Park
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    ABSTRACT: Semi-supervised learning aims to utilize unlabeled data in the process of supervised learning. In particular, combining semi-supervised learning with dimension reduction can reduce overfitting caused by small sample size in high dimensional data. By graph representation with similarity edge weights among data samples including both labeled and unlabeled data, statistical and geometricstructures in data are utilized to explore clustering structure of a small number of labeled data samples. However, most of semi-supervised dimension reduction methods use the information induced from unlabeled data points to modify only within-class scatter of labeled data, since unlabeled data can not give any information about distance between classes. In this paper, we propose semi-supervised dimension reduction which reinforcebetween-class distance by using a penalty graph and minimize within-class scatter by using a similarity graph. We apply our approach to extend linear dimension reduction methods such as Linear discriminant analysis (LDA) and Maximum margin criterion (MMC) and demonstrate that modifying between-class distance as well can make great impacts on classification performance.
    Ninth IEEE International Conference on Computer and Information Technology, Xiamen, China, CIT 2009, 11-14 October 2009, Proceedings, Volume I; 01/2009