• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2031-2041
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• 1999

In this paper, we present the HVIS (Hybrid Video Information System) which bolsters up meaning retrieval of all the various users by integrating feature-based retrieval and annotation-based retrieval of unformatted formed and massive video data. HVIS divides a set of video into video document, sequence, scene and object to model the metadata and suggests the Two layered Hybrid Object-oriented Metadata Model(THOMM) which is composed of raw-data layer for physical video stream, metadata layer to support annotation-based retrieval, content-based retrieval, and similarity retrieval. Grounded on this model, we presents the video query language which make the annotation-based query, content-based query and similar query possible and Video Query Processor to process the query and query processing algorithm. Specially, We present the similarity expression to appear degree of similarity which considers interesting of user. The proposed system is implemented with Visual C++, ActiveX and ORACLE.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.83-90
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• 2010

We describe a novel method for improving the performance of the UCC retrieval using content reputation and user reputation. The UCC retrieval is a part of the information retrieval. The goal of the information retrieval system finds documents what users want, so the goal of the UCC retrieval system tries to find UCCs themselves instead of documents. Unlike the document, the UCC has not enough textual information. Therefore, we try to use the content reputation and the user reputation based on non-textual information to gain improved retrieval performance. We evaluate content reputation using the information of the UCC itself and social activities between users related with UCCs. We evaluate user reputation using individual social activities between users or users and UCCs. We build a network with users and UCCs from social activities, and then we can get the user reputation from the network by graph algorithms. We collect the information of users and UCCs from YouTube and implement two systems using content reputation and user reputation. And then we compare two systems. From the experiment results, we can see that the system using content reputation outperforms than the system using user reputation. This result is expected to use the UCC retrieval in the feature.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.43-50
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• 2007

Delay in the process of Information Extraction, IE, is largely due to inability to correctly recognize the user's information requirement of particular search factors. Especially if the wrapper rules are used in a search engine, the search generally fails to classify internet documents properly and efficiently since the application of the same wrapper rules lacks extensibility throughout various types of existing internet document. In case of buying or selling a ship, if the price range, type. place of delivery, inspection site and other information relevant to the sales would be available through the internet for proper retrieval the sales could more readily succeed by using Ontology relating to sales or purchase information and by selectively searching for the desired information through the content based retrieval system. This system proposes to improve various wrapper systems existing throughout different internet sites and to eliminate unnecessary information tagged on the existing internet documents in order to create a more advanced information retrieval system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.949-954
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• 2005

From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. Dey could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram(ACH) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that ACH's can give superior results to color histograms for image retrieval.

• Journal of the Semiconductor & Display Technology
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• v.6 no.2 s.19
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• pp.53-57
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• 2007

In this paper, a new content-based color image retrieval method is proposed, in which both the color content and the spatial relationship of image have been taken into account. In order to represent the spatial distribution information of image, a disorder matrix, which has the invariance to the rotation and translation of the image content, has been designed. This is based on multi-resolution color-spatial information. We present our algorithm in the following section, and then verified the search results with comparison to other methods, such as color histogram, wavelet histogram, correlogram and wavelet correlogram. Experimental results with various types of images show that the proposed method not only achieves a high image retrieval performance but also improve the retrieval precision.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.85-90
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• 2006

In this paper, we propose the content-based music information retrieval system with improved retrieval speed and stable performance while maintaining resonable retrieval accuracy In order to solve the in-stable system problem multi-feature clustering (MFC) is used to setup robust music DB. In addition, the music retrieval speed was improved by using the Superclass concept. Effectiveness of the system with SuperClass and without SuperClass is compared in terms of retrieval speed, accuracy and retrieval precision. It is demonstrated that the use of WC and Superclass substantially improves music retrieval speed up to $20\%\~40\%$ while maintaining almost equal retrieval accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.4
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• pp.9-16
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• 2002

Content based Image retrieval methods in the multimedia information retrievals use primary visual features such as color, texture and shape. Color and texture generally are used as features of the image retrieval systems. But these systems may produce errors in similar image retrieval because two images with different shapes can represent very different contents. Therefore, the use of shape describing features is essential in an efficient content based image retrieval system. In this paper, after the global features filtering process by the boundary of objects, we have created a better shape similarity image retrieval system by a histogram of shape information.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.2
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• pp.9-15
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• 2000

In this paper, we proposed a content-based image retrieval method using color and object's complexity for indexing of image database. Generally, the retrieval methods using color feature can not sufficiently include the spatial information in the image. So they are reduced retrieval efficiency. Then we combined object's complexity which extracted from chain code and the conventional color feature. As a result, experiments shooed that the proposed method which considers the shape feature improved performance in conducting content-based search.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.145-156
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• 2003

Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color. shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.211-227
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• 2011

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.