• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.489-494
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• 2009

Since multimedia content is often independently encoded into multiple variations having diverse bandwidths, resolutions and compression formats, the same segment might be stored at different temporal positions within the variations. In this paper, we present a durable multimedia bookmark mechanism which provides a convenient way of switching to any variation before or during playback of the multimedia content, without experiencing temporal discontinuity or overlapping a portion of the content. We also present a new multimedia bookmark player with which users can manage a personal collection of bookmarks with an intuitive visual interface.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.405-408
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• 2000

최근 인터넷의 발전으로 많은 정보와 지식을 우리는 인터넷에서 제공받을 수 있게되었다. 인터넷에 존재하는 정보는 수많은 웹서버에 산재되어 있으며, 정보의 위치는 주소(URL)를 가지고 존재하게 되는데 사용자는 자신이 관심있는 정보의 주소를 저장하기 위하여 웹브라우저 북마크(Bookmark)기능을 사용한다. 그러나 북마크 기능은 웹문서의 주소 저장에 일차적인 목적을 두고 있으며, 이후 북마크의 개수가 증가하면, 사용자는 북마크관리가 어렵게되므로 사용자 북마크 파일을 자동으로 분류하여 관리할수 있는 에이전트 기술을 사용하고자 한다. 대표적인 분류에이전트 시스템으로는 전자우편 분류 에이전트인 Maxims, 뉴스기사 분류 에이전트인 NewT, 엔터테인먼트(Entertainment) 선별 에이전트인 Ringo 등이 있다. 이러한 시스템들은 분류할 대상에 따라 조금씩 다른 모습의 에이전트 기능을 보이고 있으며, 본 논문은 기계학습 이론중 교사학습 알고리즘인 나이브 베이지안 학습방법(Naive Bayesian Learning method)을 사용하여 사용자가 분류하지 못한 북마크를 자동으로 분류하는 단일 에이전트 기반 북마크 분류기를 설계, 구현하고자한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.35-38
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• 2002

인터넷을 항해하는 동안, 사용자들은 관심있고 흥미있는 사이트들을 자신의 즐겨찾기 폴더에 저장하여 다음에 쉽게 접속을 할 수 있도록 한다. 왜냐하면, 수많은 사이트들을 기억할 수 없기 때문이다. 본 논문에서는 북마크 기능을 이용하여 단순히 즐겨찾기 폴더에 저장된 사이트들을 보여주고 관리하는 사용자관점이 아닌, 서버측면에서 사용자의 행태를 분석하는 방법을 제안하고자 한다.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1245-1255
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• 2010

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.567-576
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• 2011

In current Web 2.0 environment, one of the most core technology is social bookmarking which users put tags and bookmarks to their interesting Web pages. The main purpose of social bookmarking is an effective information service by use of retrieval, grouping and share based on user's bookmark information and tagging result of their interesting Web pages. But, current social bookmarking system uses the number of bookmarks and tag information separately in information retrieval, where the number of bookmarks stand for user's degree of interest on Web contents, information retrieval, and classification serve the purpose of tag information. Because of above reason, social bookmarking system does not utilize effectively the bookmark information and tagging result. This paper proposes a Web contents ranking algorithm combining bookmarks and tag information, based on preceding research on associative tag extraction by tag clustering. Moreover, we conduct a performance evaluation comparing with existing retrieval methodology for efficiency analysis of our proposed algorithm. As the result, social bookmarking system utilizing bookmark with tag, key point of our research, deduces a effective retrieval results compare with existing systems.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.104-112
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• 2010

Social bookmarking is one of the most interesting trends in the current web environment. In a social bookmarking system, users annotate a web page with tags, which describe the contents of the page. Numerous studies have been done using this information, mostly on enhancing the quality of web search. In this paper, we use this information to measure the semantic similarity between two web pages. Since web pages consist of various types of multimedia data, it is quite difficult to compare the semantics of two web pages by comparing the actual data contained in the pages. With the help of social bookmarks, this comparison can be performed very effectively. In this paper, we propose a new similarity measure between web pages, called Web Page Similarity Based on Entire Tags (WSET), based on social bookmarks. The experimental results show that the proposed measure yields more satisfactory results than the previous ones.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1878-1890
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• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.27-34
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• 2011

In this paper, it was presented a design on the dynamic multimedia fingerprinting code for anti-collusion code(ACC) in the protection of multimedia content. Multimedia fingerprinting code for the conventional ACC, is designed with a mathematical method to increase k to k+1 by transform from BIBD's an incidence matrix to a complement matrix. A codevector of the complement matrix is allowanced fingerprinting code to a user' authority and embedded into a content. In the proposed algorithm, the feature points were drawing from a content which user bought, with based on these to design the dynamical multimedia fingerprinting code. The candidate codes of ACC which satisfied BIBD's v and k+1 condition is registered in the codebook, and then a matrix is generated(Below that it calls "Rhee matrix") with ${\lambda}+1$ condition. In the experimental results, the codevector of Rhee matrix based on a feature point of the content is generated to exist k in the confidence interval at the significance level ($1-{\alpha}$). Euclidean distances between row and row and column and column each other of Rhee matrix is working out same k value as like the compliment matrices based on BIBD and Graph. Moreover, first row and column of Rhee matrix are an initial firing vector and to be a forensic mark of content protection. Because of the connection of the rest codevectors is reported in the codebook, when trace a colluded code, it isn't necessity to solve a correlation coefficient between original fingerprinting code and the colluded code but only search the codebook then a trace of the colluder is easy. Thus, the generated Rhee matrix in this paper has an excellent robustness and fidelity more than the mathematically generated matrix based on BIBD as ACC.