• Journal of Korean Library and Information Science Society
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• v.18
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• pp.91-138
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• 1991

The paper describes the application areas of the hypertext and the relevance of hypertext principles to the information retrieval system. As to the techniques of the hypertext information retrieval the various navigation method including a guided tour, a history list, a browser, a book-mark, etc. are discussed. The query system is considered as the other technique to be integrated into the hypertext system for the enhancement of the interactive function of the information retrieval. Based on the theoretical background, a conceptual model of hypertext information retrieval system was constructed using GUIDE which was developed by P.J. Brown of Kent University. 16 bibliographic records from LISA of 1991(June) were used for the illustration of the basic operation of the system. Though the study could not reach the implementation level due to the absolute constraints of the time and experimental environments, further efforts will continue to develop a prototype system of a hypertext information retrieval. A few conclusions can be derived from the study : (1) The integration of the hypertext into the information retrieval system can be justified by permitting the end-users to have much stronger and more flexible interaction with the system. (2) The more the degree of the sofistication of the existing information retrieval system is the more the possibility of the development of an effective and user-oriented information retrieval system will be greater by integrating guide as a front end system to the underlying software. (3) The deep knowledge about the functions of information retrieval which can be enhanced by the hypertext could be acquired by the information retrieval specialists.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.631-644
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• 2002

In this paper efficient algorithms for content-based video retrieval using motion information are proposed, including temporal scale-invariant retrieval and temporal scale-absolute retrieval. In temporal scale-invariant video retrieval, the distance transformation is performed on each trail image in database. Then, from a given que교 trail the pixel values along the query trail are added in each distance image to compute the average distance between the trails of query image and database image, since the intensity of each pixel in distance image represents the distance from that pixel to the nearest edge pixel. For temporal scale-absolute retrieval, a new coding scheme referred to as Motion Retrieval Code is proposed. This code is designed to represent object motions in the human visual sense so that the retrieval performance can be improved. The proposed coding scheme can also achieve a fast matching, since the similarity between two motion vectors can be computed by simple bit operations. The efficiencies of the proposed methods are shown by experimental results.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.74-79
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• 2007

In this paper, we report our recent work on a indexing and retrieval system of broadcast news using audio content analysis. Key issues addressed in this work are two major parts of the audio indexing system: anchorperson detection based on audio segmentation, and phone-based spoken document retrieval, developed in the framework of the emerging MPEG-7 standard. Experiments are conducted on a database of Britisch broadcast news videos. We discuss the development of the retrieval system, and the evaluation of each part and the retrieval system.

• International Journal of Contents
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• v.8 no.2
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• pp.1-6
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• 2012

Recently, content-based music information retrieval (MIR) systems for mobile devices have attracted great interest. However, music retrieval systems are greatly affected by background noise when music is recorded in noisy environments. Therefore, we evaluated various pre-processing methods using the Philips method to determine the one that performs most robust music retrieval in such environments. We found that dynamic noise reduction (DNR) is the best pre-processing method for a music retrieval system in noisy environments.

• Journal of Korean Library and Information Science Society
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• v.31 no.1
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• pp.229-258
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• 2000

The growth of digital image an video archives is increasing the need for tools that efficiently search through large amount of visual dta. Retrieval of visual data is important issue in multimedia database. We are using contented-based visual data retrieval method for efficient retrieval of visual data. In this paper, we introduced fundamental techniques using characteristic values of image data and indexing techniques required for content-based visual retrieval. In addition we introduced content-based visual retrieval system for use of digital library.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.207-211
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• 2000

For efficient massive image retrieval, an image retrieval requires that several important objectives are satisfied, namely: automated extraction of features, efficient indexing and effective retrieval. In this work, we present a technique for extracting the 4-dimension directional feature. By directional detail, we imply strong directional activity in the horizontal, vertical and diagonal direction present in region of the image texture. This directional information also present smoothness of region. The 4-dimension feature is only indexed in the 4-D space so that complex high-dimensional indexing can be avoided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1309-1312
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• 1993

We present a flexible retrieval system of face photographs based on their linguistic descriptions in terms of fuzzy perdicates. While natural for describing a face, linguistic expressions are also subjective, which affects the retrieval result. Thus, the capability of a retrieval system to adjust to different users becomes very important. In this research we use fuzzy logic techniques, for describing image data, inference for retrieval and adjustment to a new user. Experimental results of the adjustment are also included.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.926-930
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• 2006

Current image retrieval techniques have shortcomings that make it difficult to search for images based on a semantic understanding of what the image is about. Since an image is normally associated with multiple contexts (e.g. when and where a picture was taken,) the knowledge of these contexts can enhance the quantity of semantic understanding of an image. In this paper, we present a context-aware image retrieval system, which uses the context information to infer a kind of metadata for the captured images as well as images in different collections and databases. Experimental results show that using these kinds of information can not only significantly increase the retrieval accuracy in conventional content-based image retrieval systems but decrease the problems arise by manual annotation in text-based image retrieval systems as well.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• Journal of the Korean Society for information Management
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• v.10 no.1
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• pp.31-52
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• 1993

This study compared the retrieval effectiveness of conventional Boolean and fuzzy set retrieval strategies through a retrieval experiment. The recall ratio of fuzzy set retrieval is 75%, higher than the 60% of Boolean retrieval. On the other hand, the precision ratio of fuzzy set retrieval is 69%, a little bit lower than the 73% of Boolean retrieval.