• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.1051-1066
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• 2000

According to the recent advances of digital encoding technologies and computing power, large amounts of multimedia informations such as image, graphic, audio and video are fully used in multimedia systems through Internet. By this, diverse retrieval mechanisms are required for users to search dedicated informations stored in multimedia systems, and especially it is preferred to use contents-based retrieval method rather than text-type keyword retrieval method. In this paper, we propose a new contents-based indexing and searching algorithm which aims to get both high efficiency and high retrieval performance. To achieve these objectives, firstly the proposed algorithm classifies images by a pre-processing process of edge extraction, range division, and multiple filtering, and secondly it searches the target images using spatial and textural characteristics of colors, which are extracted from the previous process, in a image. In addition, we describe the simulation results of search requests and retrieval outputs for several images of company's trade-mark using the proposed contents-based retrieval algorithm based on wavelet.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.153-163
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• 2019

This paper analyzes the film in a quantitative method to understand the principle to assign film's narrative as an art form. To do this, we tried to quantify 'conflict', which is a key factor of narrative, according to measuring the information represented in images. And thus, we measured the face areas of Protagonist and Antagonist, and then analyzed the relationship between area and conflict. The reason why the face area is used as the measurement target is because it is an index showing the way in which the director represents the conflict in the film. The quantification of conflict in films can be applied to indexing the narrative structure or specific conflict sections based on objective figures. In addition, it is possible to visualize narrative through conflict graphs, and it is meaningful that the computational criticism can apply into narrative study by quantification of conflict.

• The Journal of the Petrological Society of Korea
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• v.9 no.4
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• pp.205-210
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• 2000

Measurements of phase identification and degree of orientation for fine-grained (about 0.3 mm in diameter) minerals in rock samples performed by micro-area X-ray diffractometer.$Al_{2}SiO_{5}$ polymorphs (andalusite, kyanite and sillimanite) were chosen for the measurements and target minerals were existed on thin sections. Micro-area X-ray diffractometer is composed of 3(${\omega}\;{\chi}\;{\phi}$)-circle oscillating goniometer and position sensitive proportional counter (PSPC). $CuK_{\alpha}$ radiation was used as X-ray source and a pin hole ($50\;\mu\textrm{m}$$ in diameter) collimator was selected to focus radiation X-ray onto the target minerals. Phase identification and diffracted X-ray peak indexing were carried out by 3(${\omega}\;{\chi}\;{\phi}$)-circle oscillation measurement. Then, 2(${\omega}\;{\phi}$)-circle oscillation measurement was made for the purpose of searching the prevailing lattice plane of the minerals on thin section surface. Finally, for a selected peak by 2-circle oscillation measurement, X-ray pole figure measurement was executed for the purpose of check the degree of orientation of the single lattice direction and examine its pole distribution. As a result of 3-circle oscillation measurement, it was possible that phase identification among $Al_{2}SiO_{5}$ polymorphs. And from the results of 2-circle oscillation measurement and X-ray pole figure measurement, we recognized that poles of andalusite (122), kyanite (200) and sillimanite (310) lattice plances were well developed with direction normal to each mineral surface plane respectively. Therfore, the measurements used with micro-area X-ray diffractometer in this study will be a useful tool of phase identification and degree of orientation measurement for fine-grained rock forming minerals.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.23-30
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• 2006

A recommender system for E-commerce site receives information from customers about which products they are interested in, and recommends products that are likely to fit their needs. In this paper, we investigate several methods for large-scale product purchase data for the purpose of producing useful recommendations to customers. We apply the traditional data mining techniques of cluster analysis and collaborative filtering(CF), and CF with reduction of product-dimensionality by use of latent semantic indexing(LSI). If reduced product-dimensionality obtained from LSI shows a similar latent trend of customers for buying products to that based on original customer-product purchase data, we expect less computational effort for obtaining the nearest-neighbor for target customer may improve the efficiency of recommendation performance. From simulation experiments on synthetic customer-product purchase data, CF-based method with reduction of product-dimensionality presents a better performance than the traditional CF methods with respect to the recall, precision and F1 measure. In general, the recommendation quality increases as the size of the neighborhood increases. However, our simulation results shows that, after a certain point, the improvement gain diminish. Also we find, as a number of products of recommendation increases, the precision becomes worse, but the improvement gain of recall is relatively small after a certain point. We consider these informations may be useful in applying recommender system.