• International Journal of Knowledge Content Development & Technology
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• v.8 no.3
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• pp.53-67
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• 2018

Local languages based information storage and retrieval system is essential for any online digital repository system. This paper reports the development of an interface in Bengali that allows users not only browsing and searching Indic-script based documents but also allows administrator performing various system level operations. This paper briefly describes the origin and key characteristics of Indic-scripts along with their encoding in Unicode standard with special reference to Bengali language. It also demonstrates the development processes of Bengal-script based information representation and retrieval (IRR) system viz. BURA (Burdwan University Research Archive) using different open standard and open source software (OSS) including different factors essential for building such successful Indic-script based multilingual digital libraries. The suggested strategies may help digital library developers to design an appropriate multi-script based information access services in any other Indic-script based languages.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.720-732
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• 1996

Spatial match retrieval methods for iconic image databases recognize an image document as several icon symbols. Therefore the iconic symbols are used as primary keys to index the image document. When a user requires content-based retrieval ofimages, a spatial match retrieval method converts a query image into iconic symbols and then retrieves relevant images by accessing stored images. In order to support content-based image retrieval efficiently, we, in this paper, propose spatial match retrieval methods using signatures for iconic image databases. For this, we design new index representations of two-dimensional iconic images and explain implemented system.. In addition, we compare the conventional 9-DLT and our two-dimensional image retrieval method in terms of retrieval precision and recall ratio. We show that our method is more efficient than the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.22-29
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• 2000

The purpose of this paper is to propose a method to maximize a content-based image retrieval efficiency in multiple images. To perform an image retrieval job efficiently, it is necessary to minimize the number of candidate-images. Furthermore, a miximum efficiency of image retrieval could not be expected if an image retrieval job in the multiple images is done on the basis of patterns of single image distinctive features. In this method, a multiple SOFM neural network system is adopted to select automatically distinctive feature patterns which have a maximum efficiency of image retrieval in the multiple images. In this method. an image retrieval efficiency is improved 3% than individual features and the number of candidate-images is reduced by the multiple SOFM neural network system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.171-180
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• 2003

A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.719-725
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• 2003

The content-based image retrieval system extracts features of color, shape and texture from raw images, and builds the database with those features in the indexing process. The search in the whole retrieval system is defined as a process which finds images that have large similarity to query image using the feature database. This paper proposes a new two-stage search method in the content-based image retrieval system. The method is that the features are condensed and stored by the property of Cauchy-Schwartz inequality in order to reduce the similarity computation time which takes a mostly response time from entering a query to getting retrieval results. By the extensive computer simulations, we have observed that the proposed two-stage search method successfully reduces the similarity computation time while maintaining the same retrieval relevance as the conventional exhaustive search method. We also have observed that the method is more effective as the number of images and dimensions of the feature space increase.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.16 no.2
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• pp.157-175
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• 2005

The main purpose of this paper is to explain and analyze visual descriptors of MPEG-7 for representing multimedia content. This study describes MPEG-7 visual descriptors that are made of color, shape, texture, and motion using some examples and application areas in detail. As a result, those visual descriptors can represent the rich and deep features in multimedia contents domain. Also, the use of those descriptors will increase the retrieval effectiveness as well as the interoperability among systems through the consistency of the content representation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.532-535
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• 2008

The requirement of Digital Culture Content(Movie, Music, Animation, Digital TV, Exhibition and etc.) is increasing so variety and quantity of content is also increasing. The Movie what majority of the digital Content is developing of technology and data. In the result, the efficient retrieval service has required and user want to use a recommendation engine and semantic retrieval methods through the recommendation system. Therefore, this paper will suggest analysing trait element of digital content data, building of retrieval technology, analysing and retrieval technology base on KANSEI vocabulary and etc. For the these, we made a extraction technology of trait element based on semantics and KANSEI processing algorithm based on color information.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.42-45
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• 2002

A novel characteristic value extraction method based on morphological spatial frequency is proposed. Morphological spatial frequency defined by morphological pattern distribution function is introduced. Superiority of the method was proved for various images by experiment. Furthermore the fact that the proposed method does not need threshold to obtain binary image provides its applicability to content-based image retrieval.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.3-8
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• 2002

This paper introduces the basic concept of computational modeling of perception processes for multimedia data. Such processes are modeled as hierarchical inter- and intra- relationships amongst information in physical, physiological, psychological and cognitive layers in perception. Based on our framework, this paper gives the algorithms for content-based retrieval for multimedia database systems.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.507-520
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• 2003

In this paper, an automatic construction method of theme melody index for large music database and an associative content-based music retrieval mechanism in which the constructed theme melody index is mainly used to improve the users' response time are proposed. First, the system automatically extracted the theme melody from a music file by the graphical clustering algorithm based on the similarities between motifs of the music. To place an extracted theme melody into the metric space of M-tree, we chose the average length variation and the average pitch variation of the theme melody as the major features. Moreover, we added the pitch signature and length signature which summarize the pitch variation pattern and the length variation pattern of a theme melody, respectively, to increase the precision of retrieval results. We also proposed the associative content-based music retrieval mechanism in which the k-nearest neighborhood searching and the range searching algorithms of M-tree are used to select the similar melodies to user's query melody from the theme melody index. To improve the users' satisfaction, the proposed retrieval mechanism includes ranking and user's relevance feedback functions. Also, we implemented the proposed mechanisms as the essential components of content-based music retrieval systems to verify the usefulness.