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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 13B, Issue 7 - Dec 2006
Volume 13B, Issue 6 - Dec 2006
Volume 13B, Issue 5 - Oct 2006
Volume 13B, Issue 4 - Aug 2006
Volume 13B, Issue 3 - Jun 2006
Volume 13B, Issue 2 - Apr 2006
Volume 13B, Issue 1 - Feb 2006
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A Quantitative Trust Model based on Empirical Outcome Distributions and Satisfaction Degree
Kim, Hak-Joon ; Sohn, Bong-Ki ; Lee, Seung-Joo ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 633~642
DOI : 10.3745/KIPSTB.2006.13B.7.633
In the Internet environment many interactions between many users and unknown users take place and it is usually rare to have the trust information about others. Due to the lack of trust information, entities have to take some risks in transactions with others. In this perspective, it is crucial for the entities to be equipped with functionality to accumulate and manage the trust information on other entities in order to reduce risks and uncertainty in their transactions. This paper is concerned with a quantitative computational trust model which takes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust for an entity. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the empirical outcome outcome distributions and the entity`s preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper also shows that the model could help the entities effectively choose other entities for transactions with some experiments in e-commerce.
A Spatial Entropy based Decision Tree Method Considering Distribution of Spatial Data
Jang, Youn-Kyung ; You, Byeong-Seob ; Lee, Dong-Wook ; Cho, Sook-Kyung ; Bae, Hae-Young ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 643~652
DOI : 10.3745/KIPSTB.2006.13B.7.643
Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.
Emotion Recognition Based on Facial Expression by using Context-Sensitive Bayesian Classifier
Kim, Jin-Ok ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 653~662
DOI : 10.3745/KIPSTB.2006.13B.7.653
In ubiquitous computing that is to build computing environments to provide proper services according to user`s context, human being`s emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user`s context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.
Resolving Occlusion Technique of Virtual Target on Real Image using DEM
Cha, Jeong-Hee ; Jang, Hyo-Jong ; Kim, Gye-Young ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 663~670
DOI : 10.3745/KIPSTB.2006.13B.7.663
For virtual target to be displaying on real image realistically, it is essential to determine the location of the virtual object together with producing the occlusions area after registering two world. In this paper, we propose the new method to solve occlusions which happens during virtual target moves according to the simulated route on real image. For this purpose, we first construct three dimensional virtual world by DEM of experimental area and register CCD camera image on it by visual clues. Next, we also propose a method to solve the occlusion using snake and picking algorithm which can extract the three dimensional information of the position happening occlusion in the image and can use it when target moves that area. In the experiment, we proved the effectiveness of the proposed method in the environment which a partial occlusions happens.
Robust Estimation of Camera Motion using Fuzzy Classification Method
Lee, Joong-Jae ; Kim, Gye-Young ; Choi, Hyung-Il ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 671~678
DOI : 10.3745/KIPSTB.2006.13B.7.671
In this paper, we propose a method for robustly estimating camera motion using fuzzy classification from the correspondences between two images. We use a RANSAC(Random Sample Consensus) algorithm to obtain accurate camera motion estimates in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier ratio. To resolve this problem the proposed method classifies samples into three classes(good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. The experimental results show that the proposed approach is very effective for computing a homography.
Protein-Protein Interaction Reliability Enhancement System based on Feature Selection and Classification Technique
Lee, Min-Su ; Park, Seung-Soo ; Lee, Sang-Ho ; Yong, Hwan-Seung ; Kang, Sung-Hee ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 679~688
DOI : 10.3745/KIPSTB.2006.13B.7.679
Protein-protein interaction data obtained from high-throughput experiments includes high false positives. In this paper, we introduce a new protein-protein interaction reliability verification system. The proposed system integrates various biological features related with protein-protein interactions, and then selects the most relevant and informative features among them using a feature selection method. To assess the reliability of each protein-protein interaction data, the system construct a classifier that can distinguish true interacting protein pairs from noisy protein-protein interaction data based on the selected biological evidences using a classification technique. Since the performance of feature selection methods and classification techniques depends heavily upon characteristics of data, we performed rigorous comparative analysis of various feature selection methods and classification techniques to obtain optimal performance of our system. Experimental results show that the combination of feature selection method and classification algorithms provide very powerful tools in distinguishing true interacting protein pairs from noisy protein-protein interaction dataset. Also, we investigated the effects on performances of feature selection methods and classification techniques in the proposed protein interaction verification system.
A Histogram Matching Scheme for Color Pattern Classification
Park, Young-Min ; Yoon, Young-Woo ;
The KIPS Transactions:PartB, volume 13B, issue 7, 2006, Pages 689~698
DOI : 10.3745/KIPSTB.2006.13B.7.689
Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.