• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1165-1173
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• 2012

One of the most popular technologies in Web2.0 is tagging, and it widely applies to Web content as well as multimedia data such as image and video. Web users have expected that tags by themselves would be reused in information search and maximize the search efficiency, but wrong tag by irresponsible Web users really has brought forth a incorrect search results. In past papers, we have gathered various information resources and tags scattered in Web, mapped one tag onto other tags, and clustered these tags according to the corelation between them. A 3-tag based search algorithm which use the clustered tags of past papers, is proposed in this paper. For performance evaluation of the proposed algorithm, our algorithm is compared with image search result of Flickr, typical tag based site, and is evaluated in accuracy and recall factor.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.113-122
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• 1999

When we want to retrieve the most similar image from the image database, the color histogram intersection, shape feature and texture feature comparing method are used as a metric to measure the similarity. In order to increase the accuracy of retrievals, we need to integrate two different features. In this paper, the histogram intersection and shape based block histogram intersection method are used. This method results in a high efficient algorithm that meets a similar accuracy and a relatively fast retrieval speed compared to the method of integration of two different features. The Proposed algorithm is tested on retrievals of image database consisting of various 600 images and we implemented that the proposed algorithm gives fast, high efficiency and reliability compared to others.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.57-64
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• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the overwhelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• The Journal of Korean Association of Computer Education
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• v.12 no.2
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• pp.33-45
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• 2009

We can communicate with each other using notice board, questions & answers boards, messages in the web-based learning system, especially questions & answers board are the places that can be shared with the learning experiences between the learners and improve one's learning efficiency. In this study, we found out the problems when studying the learning contents and learning questions & answers boards in the web-based learning system and proposed a web-based learning system consisted of learning contents and the questions & answers boards with ability for searching automatically and providing questions & answers that is related with the learning contents. According to the result of the effectiveness and accuracy analysis, the proposed web-based learning system can be very useful and improve one's learning achievements by searching exactly the learning questions & answers.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.650-661
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• 2005

As the Internet is growing, the use of XML which is a standard of semi-structured document is increasing. Therefore, there are on going works about integration and retrieval of XML documents. However, the basis of efficient integration and retrieval of documents is to cluster XML documents with similar structure. The conventional XML clustering approaches use the hierarchical clustering algorithm that produces the demanded number of clusters through repeated merge, but it have some problems that it is difficult to compute the similarity between XML documents and it costs much time to compare similarity repeatedly. In order to address this problem, we use clustering algorithm for transactional data that is scale for large size of data. In this paper we use common structures from XML documents that don't have DTD or schema. In order to use common structures of XML document, we extract representative structures by decomposing the structure from a tree model expressing the XML document, and we perform clustering with the extracted structure. Besides, we show efficiency of proposed method by comparing and analyzing with the previous method.

• The KIPS Transactions:PartD
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• v.13D no.3 s.106
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• pp.309-316
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• 2006

We propose a novel technique to categorize XML documents and extract a concept efficiently using domain ontology. First, we create domain ontology that use text mining technique and statistical technique. We propose a DScore technique to classify XML documents by using the structural characteristic of XML document. We also present TScore technique to extract a concept by comparing the association term set of domain ontology and the terms in the XML document. To verify the efficiency of the proposed technique, we perform experiment for 295 papers in the computer science area. The results of experiment show that the proposed technique using the structural information in the XML documents is more efficient than the existing technique. Especially, the TScore technique effectively extract the concept of documents although frequency of term is few. Hence, the proposed concept-based retrieval techniques can be expected to contribute to the development of an efficient ontology-based knowledge management system.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.501-509
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• 2002

SGML(ISO 8879) has been proliferated to support various document styles and to transfer documents into different platforms. SGML documents have logical structure information in addition to contents. As SGML documents are widely used, there is an increasing need for database storage and retrieval system using the logical structure of documents. However. traditional search engines using document indexes cannot exploit the logical structure. In this Paper, we have developed an SGML document storage system, which is DTD-independent and store the document type and the document instance separately by using Grove which is the document model for DSSSL and HyTime. We have used the Object Store, an object-oriented DBMS, to store the structure information appropriately without any loss of structural information. Also, we have supported a index structure for search efficiency like the relational DBMS, and constructed an effective user interface which combines content-based search with structure-based search.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3102-3119
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• 2021

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.31-37
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• 2021

In this paper, we propose a method to effectively search by automatically classifying scenes in which specific images such as pitching or swing appear in baseball game images using a CNN(Convolution Neural Network) model. In addition, we propose a video scene search system that links the classification results of specific motions and game records. In order to test the efficiency of the proposed system, an experiment was conducted to classify the Korean professional baseball game videos from 2018 to 2019 by specific scenes. In an experiment to classify pitching scenes in baseball game images, the accuracy was about 90% for each game. And in the video scene search experiment linking the game record by extracting the scoreboard included in the game video, the accuracy was about 80% for each game. It is expected that the results of this study can be used effectively to establish strategies for improving performance by systematically analyzing past game images in Korean professional baseball games.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.111-124
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• 2011

The apartment' share of the housing has steadily increased and property assets have been valued in importance as the one of asset value. Information retrieval system using internet is particularly active in the real estate market. However, user satisfaction on real estate information system is not very high, and there is a lack of research on real estate retrieval to increasing efficiency until now. This study presents a new knowledge information system developed to consider region-related factor and individual-related factor in the real estate market. In addition it enables a real estate knowledge system to search various preferential requirements for buyers such as school district, living convenience, easy maintenance as well as price. We made a survey of the search condition preference of experts on 30 real estate agents and then analyzed the result using AHP methodology. Furthermore, this research is to build apartment ontology using semantic web technologies to standardize various terminologies of apartment information and to show how it can be used to help buyers find apartments of the interest. After designing architecture of a real estate knowledge information system, this system is applied to the Busan real estate market to estimate the solutions of retrieval through Multi-Attribute Decision Making(MADM). Based on the results of the analysis, we endowed the buyer and expert's selected factors with weights in the system. Evaluation results indicate that this new system is to raise not only the value satisfaction of user, but also make it possible to effectively search and analyze the real estate through entropy analysis of MADM. This new system is to raise not only the value satisfaction of buyer's real estate, but also make it possible to effectively search and analyze the related real estate, consequently saving the searching cost of the buyers.