• Journal of the Korean Society for Library and Information Science
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• v.44 no.3
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• pp.81-102
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• 2010

The purpose of this study was to investigate college students' preferences of Web-based OPAC retrieval techniques and their ABO blood types as an empirical survey. Data was collected through a self-designed questionnaire with a total of 101 undergraduate students from the College of Social Sciences responding. The collected data was analyzed using descriptive statistics, and One-way ANOVA. The results show that 'title' was most preferred among the access points, 'AND' was the most preferred Boolean operator, 'publication year' and 'subject' were the most favored techniques in limiting the scope of retrieval, and 'record number limit per page' was the most frequently used for displaying retrieval results. The results also show that there were little(3 out of 22, i.e. 13.6%) statistically significant differences between the college students' preferences of Web-based OPAC techniques and their blood type.

• Journal of the Korean Society for information Management
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• v.12 no.2
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• pp.213-223
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• 1995

It has known that different representations of either queries or documents, or different retrieval techniques retrieve different sets of documents. Recent works suggest that significant improvements in retrieval performance can be achieved by combining multiple representations or multiple retrieval techniques. In this paper we propose a simple method for retrieving different documents within a single query representation, a single document representation and a single retrieval technique. We classify the types of documents, and describe the properties of weighting schemes. Then. we explain that different properties of weighting schemes may retrieve different types of documents. Experimental results show that significant improvements can be obtained by combining the retrieval results form different properties of weighting schemes.

• Journal of the Korean Society for information Management
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• v.8 no.2
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• pp.3-31
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• 1991

A prototype model of intelligent information retrieval system is presented with the definition of intelligent information retrieval. An intelligent information retrieval system for Korean documents was designed, and the system was implemented with Turbo Prolog 2.0 and Turbo Pascal 5.5. The characteristics of the system include natural language interface, user modeling, automatic indexing by case relationship, and multiple retrieval techniques.

• Journal of Ubiquitous Convergence Technology
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• v.1 no.1
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• pp.18-22
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• 2007

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.2
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• pp.97-115
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• 2004

The paper examined the concepts and features of content-based Image and Video retrieval systems. It then analyzed the retrieval performance of on five content_based retrieval systems in terms of usability and retrieval features. The results showed that the combination of content_based retrieval techniques and meta-data based retrieval will be able to improve the retrieval effectiveness.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1418-1432
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• 1997

Most general content_based image retrieval techniques use color and texture as retrieval indices. In color techniques, color histogram and color pair based color retrieval techniques suffer from a lack of spatial information and text. And This paper describes the design and implementation of content_based image retrieval system using color space and keywords. The preprocessor for image retrieval has used the coordinate system of the existing HSI(Hue, Saturation, Intensity) and preformed to split One image into chromatic region and achromatic region respectively, It is necessary to normalize the size of image for 200*N or N*200 and to convert true colors into 256 color. Two color histograms for background and object are used in order to decide on color selection in the color space. Spatial information is obtained using a maximum entropy discretization. It is possible to choose the class, color, shape, location and size of image by using keyword. An input color is limited by 15 kinds keyword of chromatic and achromatic colors of the Korea Industrial Standards. Image retrieval method is used as the key of retrieval properties in the similarity. The weight values of color space ${\alpha}(%)and\;keyword\;{\beta}(%)$ can be chosen by the user in inputting the query words, controlling the values according to the properties of image_contents. The result of retrieval in the test using extracted feature such as color space and keyword to the query image are lower that those of weight value. In the case of weight value, the average of te measuring parameters shows approximate Precision(0.858), Recall(0.936), RT(1), MT(0). The above results have proved higher retrieval effects than the content_based image retrieval by using color space of keywords.

• Journal of Korean Library and Information Science Society
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• v.18
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• pp.245-268
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• 1991

Some conclusions derived from the study are as follows : 1) Most conventional information retrieval systems require users do precisely that specify the information they require, but user's information needs are not always precise. 2) Information need arise from when users are in an anomalous stats of knowledge about some problem, so user's information needs are not always precise. 3) Information retrieval systems must assist to users make themselves correct, complement, and specify their information need. When information need arise, systems should understand the state of knowledge and will be design to present and specify their ill-defined potential information need. 4) Existing information retrieval techniques need a tool to complement current its disadvantages and to enhance retrieval efficiency. Browsing searching will be a role such as a tool. 5) Browsing searching can understand user's state of knowledge and assist to specify not only pre-searching information need but also changed information need during searching progress.

• Journal of Korean Library and Information Science Society
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• v.31 no.4
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• pp.191-212
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• 2000

This study is intlded to ampre the effectiveness of the Northemlight and Google, which are based on Datamining kdmique with a Metacrawler, one of metasearch engines. As a result, searches responding to queries in the Northemlight and Google produced a higher precision and recall as comparrd with searches nspcdhg to queries in the metacrawler. The results show that the Datamining techniques can help improve information retrieval effectinveness.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.