• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.614-618
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• 2008

As the multimedia data increase a lot with the rapid development of the Internet, an efficient retrieval technique focusing on individual users is required based on the analyses of such data. However, user modeling services provided by recent web sites have the limitation of text-based page configurations and recommendation retrieval. In this paper, we construct the user preference model with a Bayesian network to apply the user modeling to video retrieval, and suggest a method which utilizes probability reasoning. To do this, context information is defined in a real office environment and the video scripts acquired from established cameras and annotated the context information manually are used. Personal information of the user, obtained from user input, is adopted for the evidence value of the constructed Bayesian Network, and user preference is inferred. The probability value, which is produced from the result of Bayesian Network reasoning, is used for retrieval, making the system return the retrieval result suitable for each user's preference. The usability test indicates that the satisfaction level of the selected results based on the proposed model is higher than general retrieval method.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1160-1169
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• 1996

In this study, an experiment of information retrieval using fuzzy connection matrix of keywords was conducted. A query for retrieval was constructed from each keyword and Boolean operator such as AND, OR, NOT. In a workstation environment, the performance of the fuzzy retrieval system was proved to be considerably effective than that of the system using the crisp set theory. And both recall ratio and precision ratio showed that the proposed technique would be a possible alternative in future information retrieval. Some special features of this experimental system were ; ranking the results in the order of connectivity, making the retrieval results correspond flexibly by changing the threshold value, trying to accord the retrieval process with the retrieval semantics by treating the averse-connectivity (fuzzy value) as a semantic approximation between kewords.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.5 no.1
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• pp.85-105
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• 1981

Information is an essential factor leading the rapid progress which is one of the distinguished characteristics in modem society. As more information is required and as more is supplied by individuals, governmental units, businesses, and educational institutions, the greater will be the requirement for efficient methods of communication. One possibility for improving the information dissemination process is to use computers. The capabilities of such machine are beginning to be used in the process of Information storage, retrieval and dissemination. An important problems, that must be carefully examined is whether one technique for information retrieval is better for worse than another. This paper examines problem of how to evaluate an information retrieval system. One specific approach is a cost accounting model for use in studying how to minimize the cost of operating a mechanized retrieval system. Through the use of cost analysis, the model provides a method for comparative evaluation between systems. The general cost accounting model of the literature retrieval system being designed by this study are given below. 1. The total cost accounting model of the literature retrieval system. The total cost of the literature retrieval system = (the cost per unit of user time X the amount of user time) + ( the cost per unit of system time X the amount of system time) 2. System cost accounting model system cost = (the pre-search system cost per unit of time X time) + (the search system cost per unit of time X time) + (the post search system cost per unit of time X time) 1) Pre-search system cost per unit of time = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 2) Search system cost per unit of time = comparison cost + document representation cost. 3) Post-search system cost per unit of time. = cost of channel per unit time + cost of central processing unit per unit time + cost of storage per unit time 3. User cost accounting model Total user cost = [pre-search user cost per unit of time X (time + additional time) ] + [search user cost per unit of time X (time + additional time) ] + [post-search user cost per unit of time X (time + additional time) ].

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.47-54
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• 1998

With the increasing popularity of the use of large-volume image database in various application, it becomes imperative to build an efficient and fast retrieval system to browse through the entire database. We present a new method for a content-based image retrieval by using a variable block size and block matching algorithm. Proposed approach is reflecting image features that exploit visual cues such as color and space allocation of image and is getting the fast retrieval time by automatical convergence of retrieval times which adapt to wanting similarity value. We have implemented this technique and tested it for a database of approximately 150 images. The test shows that a 1.9 times fast retrieval time compare to J & V algorithm at the image retrieval efficiency 0.65 and that a 1.83 times fast retrieval time compare to predefined fixed block size.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.579-586
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• 2002

The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge information. In the edge descriptor, the edge information is the edge histogram derived from a gray-level value image. This paper proposes a new method which extracts color edge information from color images and a new approach for the contents-based image retrieval based on the color edge histogram. The poposed method and technique are applied to image retrieval of the e-catalogue. For the evaluation, the results of image retrieval using the proposed approach are compared with those of image retrieval using the edge descriptor by MPEG-7 and the statistics shows the efficiency of the proposed method. The proposed color edge model is made by combining the R,G,B channel components vectorially and by characterizing the vector norm of the edge map. The color edge histogram using the direction of the color edge model is subsequently used for the contents-based image retrieval.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.585-592
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• 2013

CBIR is an effective tool to search and extract image contents in a large remote sensing image database queried by an operator or end user. However, as imaging principles are different by sensors, their visual representation thus varies among image modality type. Considering images of various modalities archived in the database, image modality difference has to be tackled for the successful CBIR implementation. However, this topic has been seldom dealt with and thus still poses a practical challenge. This study suggests a cross modality CBIR (termed as the CM-CBIR) method that transforms given query feature vector by a supervised procedure in order to link between modalities. This procedure leverages the skill of analyst in training steps after which the transformed query vector is created for the use of searching in target images with different modalities. Current initial results show the potential of the proposed CM-CBIR method by delivering the image content of interest from different modality images. Despite its retrieval capability is outperformed by that of same modality CBIR (abbreviated as the SM-CBIR), the lack of retrieval performance can be compensated by employing the user's relevancy feedback, a conventional technique for retrieval enhancement.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.763-765
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• 2005

In the area of remote sensing, an immense number of images are continuously generated by various remote sensing systems. These images must then be managed by a database system efficient storage and retrieval. There are many types of image database systems, among which the content-based image retrieval (CBIR) system is the most advanced. CBIR utilizes the metadata of images including the feature data for indexing and searching images. Therefore, the performance of image retrieval is significantly affected by the storage method of the image metadata. There are many features of images such as color, texture, and shape. We mainly consider the shape feature because shape can be identified in any remote sensing while color does not always necessarily appear in some remote sensing. In this paper, we propose a metadata representation and storage method for image search based on shape features. First, we extend MPEG-7 to describe the shape features which are not defined in the MPEG-7 standard. Second, we design a storage schema for storing images and their metadata in a relational database system. Then, we propose an efficient storage method for managing the shape feature data using a Wavelet technique. Finally, we provide the performance results of our proposed storage method.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1902-1911
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• 1999

In this paper we present a content-based image retrieval algorithm using the visual feature vectors which describe the spatial characteristics of objects. The proposed technique uses the Gaussian mixture model(GMM) to represent multi-colored objects and the expectation maximization(EM) algorithm is employed to estimate the maximum likelihood(ML) parameters of the model. After image segmentation is performed based on GMM, the shape and color features are extracted from each object using Fourier descriptors and color histograms, respectively. Image retrieval consists of two steps: first, the shape-based query is carried out to find the candidate images whose objects have the similar shapes with the query image and second, the color-based query is followed. The experimental results show that the proposed algorithm is effective in image retrieving by using the spatial and visual features of segmented objects.

• The Journal of Information Systems
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• v.8 no.1
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.