Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
The KIPS Transactions:PartB
Journal Basic Information
Journal DOI :
Korea Information Processing Society
Editor in Chief :
Volume & Issues
Volume 14B, Issue 7 - Dec 2007
Volume 14B, Issue 6 - Oct 2007
Volume 14B, Issue 5 - Oct 2007
Volume 14B, Issue 4 - Aug 2007
Volume 14B, Issue 3 - Jun 2007
Volume 14B, Issue 2 - Apr 2007
Volume 14B, Issue 1 - Feb 2007
Selecting the target year
Robust 2D Feature Tracking in Long Video Sequences
Yoon, Jong-Hyun ; Park, Jong-Seung ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 473~480
DOI : 10.3745/KIPSTB.2007.14-B.7.473
Feature tracking in video frame sequences has suffered from the instability and the frequent failure of feature matching between two successive frames. In this paper, we propose a robust 2D feature tracking method that is stable to long video sequences. To improve the stability of feature tracking, we predict the spatial movement in the current image frame using the state variables. The predicted current movement is used for the initialization of the search window. By computing the feature similarities in the search window, we refine the current feature positions. Then, the current feature states are updated. This tracking process is repeated for each input frame. To reduce false matches, the outlier rejection stage is also introduced. Experimental results from real video sequences showed that the proposed method performs stable feature tracking for long frame sequences.
Implementation of a Journal's Table of Contents Separation System based on Contents Analysis
Kwon, Young-Bin ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 481~492
DOI : 10.3745/KIPSTB.2007.14-B.7.481
In this paper, a method for automatic indexing of contents to reduce effort for inputting paper information and constructing index is considered. Existing document analysis methods can't analyse various table of contents of journal paper formats efficiently because they have many exceptions. In this paper, various contents formats for journals, which have different features from those for general documents, are analysed and described. The principal elements that we want to represent are titles, authors, and pages for each papers. Thus, the three principal elements are modeled according to the order of their arrangement, and their features are extracted. And a table of content recognition system of journal is implemented, based on the proposed modeling method. The accuracy of exact extraction ratio of 91.5% on title, author, and page type on 660 published papers of various journals is obtained.
Online Multi-view Range Image Registration using Geometric and Photometric Feature Tracking
Baek, Jae-Won ; Moon, Jae-Kyoung ; Park, Soon-Yong ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 493~502
DOI : 10.3745/KIPSTB.2007.14-B.7.493
An on-line registration technique is presented to register multi-view range images for the 3D reconstruction of real objects. Using a range camera, we first acquire range images and photometric images continuously. In the range images, we divide object and background regions using a predefined threshold value. For the coarse registration of the range images, the centroid of the images are used. After refining the registration of range images using a projection-based technique, we use a modified KLT(Kanade-Lucas-Tomasi) tracker to match photometric features in the object images. Using the modified KLT tracker, we can track image features fast and accurately. If a range image fails to register, we acquire new range images and try to register them continuously until the registration process resumes. After enough range images are registered, they are integrated into a 3D model in offline step. Experimental results and error analysis show that the proposed method can be used to reconstruct 3D model very fast and accurately.
Crying and Face Color Analysis for Baby Heart Diseases Diagnosis
Cho, Dong-Uk ; Lee, Se-Hwan ; Kim, Bong-Hyun ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 503~512
DOI : 10.3745/KIPSTB.2007.14-B.7.503
An infant of a baby child who haven't communication skills through a language expresses their intention or baby condition as generally crying. Among these things, it is important to show a baby condition because their disease miss diagnosis time or remain to decide an exact diagnosis result too hard. For this, in this paper, we are going to develop system which decides where to be not good body point by analysing their face color and crying sound. Specifically, in this paper, we are going to act for baby heart diseases by doing feature extraction for their face region color and crying sound. To embody, we are going to present diagnosis method and compare analyze their crying sound a stand child, a different diseases child and a baby heart diseases child through each analyzed element. And also, we are going to extract matters to be attended to baby heart diseases through experiment and prepare objective index and an accuracy of baby heart diseases diagnosis result.
Determining the number of Clusters in On-Line Document Clustering Algorithm
Jee, Tae-Chang ; Lee, Hyun-Jin ; Lee, Yill-Byung ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 513~522
DOI : 10.3745/KIPSTB.2007.14-B.7.513
Clustering is to divide given data and automatically find out the hidden meanings in the data. It analyzes data, which are difficult for people to check in detail, and then, makes several clusters consisting of data with similar characteristics. On-Line Document Clustering System, which makes a group of similar documents by use of results of the search engine, is aimed to increase the convenience of information retrieval area. Document clustering is automatically done without human interference, and the number of clusters, which affect the result of clustering, should be decided automatically too. Also, the one of the characteristics of an on-line system is guarantying fast response time. This paper proposed a method of determining the number of clusters automatically by geometrical information. The proposed method composed of two stages. In the first stage, centers of clusters are projected on the low-dimensional plane, and in the second stage, clusters are combined by use of distance of centers of clusters in the low-dimensional plane. As a result of experimenting this method with real data, it was found that clustering performance became better and the response time is suitable to on-line circumstance.
Improved Automatic Lipreading by Stochastic Optimization of Hidden Markov Models
Lee, Jong-Seok ; Park, Cheol-Hoon ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 523~530
DOI : 10.3745/KIPSTB.2007.14-B.7.523
This paper proposes a new stochastic optimization algorithm for hidden Markov models (HMMs) used as a recognizer of automatic lipreading. The proposed method combines a global stochastic optimization method, the simulated annealing technique, and the local optimization method, which produces fast convergence and good solution quality. We mathematically show that the proposed algorithm converges to the global optimum. Experimental results show that training HMMs by the method yields better lipreading performance compared to the conventional training methods based on local optimization.
A Two-Phase Hybrid Stock Price Forecasting Model : Cointegration Tests and Artificial Neural Networks
Oh, Yu-Jin ; Kim, Yu-Seop ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 531~540
DOI : 10.3745/KIPSTB.2007.14-B.7.531
In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.
Fast Simulated Annealing with Greedy Selection
Lee, Chung-Yeol ; Lee, Sun-Young ; Lee, Soo-Min ; Lee, Jong-Seok ; Park, Cheol-Hoon ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 541~548
DOI : 10.3745/KIPSTB.2007.14-B.7.541
Due to the mathematical convergence property, Simulated Annealing (SA) has been one of the most popular optimization algorithms. However, because of its problem of slow convergence in the practical use, many variations of SA like Fast SA (FSA) have been developed for faster convergence. In this paper, we propose and prove that Greedy SA (GSA) also finds the global optimum in probability in the continuous space optimization problems. Because the greedy selection does not allow the cost to become worse, GSA is expected to have faster convergence than the conventional FSA that uses Metropolis selection. In the computer simulation, the proposed method is shown to have as good performance as FSA with Metropolis selection in the viewpoints of the convergence speed and the quality of the found solution. Furthermore, the greedy selection does not concern the cost value itself but uses only dominance of the costs of solutions, which makes GSA invariant to the problem scaling.
Reducing Noise Using Degree of Scattering in Collaborative Filtering System
Ko, Su-Jeong ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 549~558
DOI : 10.3745/KIPSTB.2007.14-B.7.549
Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.
Combining Sentimental Expression-level and Sentence-level Classifiers to Improve Subjective Sentence Classification
Kang, In-Ho ;
The KIPS Transactions:PartB, volume 14B, issue 7, 2007, Pages 559~566
DOI : 10.3745/KIPSTB.2007.14-B.7.559
Subjective sentences express opinions, emotions, evaluations and other subjective ideas relevant to products or events. These expressions sometimes can be seen in only part of a sentence, thus extracting features from a full-sentence can degrade the performance of subjective-sentence-classification. This paper presents a method for improving the performance of a subjectivity classifier by combining two classifiers generated from the different representations of an input sentence. One representation is a sentimental phrase that represents an automatically identified subjective expression or objective expression and the other representation is a full-sentence. Each representation is used to extract modified n-grams that are composed of a word and its contextual words' polarity information. The best performance, 79.7% accuracy, 2.5% improvement, was obtained when the phrase-level classifier and the sentence-level classifier were merged.