• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2073-2080
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• 2015

Even though users choose goods they want to buy in on-line shopping malls, real purchase is often performed in off-line shopping malls. It is called reverse showrooming. It means that users' analysis of goods based on images and description of internet shopping malls has limitation. Thus, large-scale online shopping malls provide a customized shopping information. However, in that case, the provided information is a simple list of goods users bought or retrieved. Thus, a system to analyze various needs of users and apply the result into on-line shopping mall is necessary. In this paper, an analysis system is proposed. The system contains a module to analyze user defined preference and a module to analyze users' reviews. The former designates two goods and collects preferences of individual users. the latter analyzes reviews about purchased goods based on database dictionary stored in advance for analyzing reviews. The system implemented shows that it is possible to recommend some goods that meet each users's needs

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.5
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• pp.59-68
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• 2015

Since interdisciplinary convergence could beyond the borders of each disciplines, it is able to create new and meaningful knowledge through collaborative research between different study areas. Especially, in recent years, the Digital Humanities has attracted the attention as the convergence form of the Humanities and ICT. From a research methodology perspective, the Digital Humanities is a tool that can be used as a convergence system for various information utilization such as storage, retrieve, share, and spread. In view of Information system, Digital Humanities has been constructed and used in a variety of systems. Among them, studies related to information visualization for the Digital Humanities have been actively conducted. To visualize data or information, various types such as images, multimedia, interface, and etc could be applied. In this paper, we analyze the cases of various information visualization in digital humanities systems, and propose a method to utilize them in the Humanities and Area Studies.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.165-193
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• 2022

It can be said that digital technology is at the center of the change in the modern work environment. In particular, in general public institutions that prove their work with records produced by business management systems and document production systems, the record management system is also the work environment itself. Gimpo City applied for the 2021 public cloud leading project of the National Information Society Agency (NIA) to proactively respond to the 4th industrial revolution technology era and implemented a public cloud-based AI-OCR technology enhancement project with 330 million won in support of 330 million won. Through this, it was converted into data beyond the limitations of non-electronic records limited to search and image viewing that depend on standardized index values. In addition, a 98% recognition rate was realized by applying a new technology called AI-OCR. Since digital technology has been used to improve work efficiency, productivity, development cost, and record management service levels of internal and external users, we would like to share the direction of enhancing expertise in the record management and implementation of work environment innovation.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.1-10
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• 2007

This paper proposes a new indexing mechanism for MPEG-7 visual descriptors, especially Dominant Color and Contour Shape descriptors, that guarantees an efficient similarity search for the multimedia database whose visual meta-data are represented with MPEG-7. Since the similarity metric used in the Dominant Color descriptor is based on Gaussian mixture model, the descriptor itself could be transform into a color histogram in which the distribution of the color values follows the Gauss distribution. Then, the transformed Dominant Color descriptor (i.e., the color histogram) is indexed in the proposed indexing mechanism. For the indexing of Contour Shape descriptor, we have used a two-pass algorithm. That is, in the first pass, since the similarity of two shapes could be roughly measured with the global parameters such as eccentricity and circularity used in Contour shape descriptor, the dissimilar image objects could be excluded with these global parameters first. Then, the similarities between the query and remaining image objects are measured with the peak parameters of Contour Shape descriptor. This two-pass approach helps to reduce the computational resources to measure the similarity of image objects using Contour Shape descriptor. This paper also proposes two integration schemes of visual descriptors for an efficient retrieval of multimedia database. The one is to use the weight of descriptor as a yardstick to determine the number of selected similar image objects with respect to that descriptor, and the other is to use the weight as the degree of importance of the descriptor in the global similarity measurement. Experimental results show that the proposed indexing and integration schemes produce a remarkable speed-up comparing to the exact similarity search, although there are some losses in the accuracy because of the approximated computation in indexing. The proposed schemes could be used to build a multimedia database represented in MPEG-7 that guarantees an efficient retrieval.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.231-238
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• 2021

SNS data can catch the user's thoughts and actions. And the trend of the search portal is a representative service that can observe the interests of users and their changes. In this paper, the relationship was analyzed by comparing statistics on narcotics incidents and the degree of exposure to narcotics related words in tweets of SNS and in the trends of search portal. It was confirmed that the trend of SNS and search portal trends was the same in the statistics of the prosecution office with a certain time difference.In addition, cluster analysis was performed to understand the meaning of tweets in which narcotics related words were mentioned. In the 50,000 tweets collected in January 2020, it was possible to find meaning related to the sale of actual drugs. Therefore, through SNS monitoring alone it is possible to monitor narcotics-related incidents and to find specific sales or purchase-related information, and this can be used in the investigation process. In the future, it is expected that crime monitoring and prediction systems can be proposed as related crime analysis may be possible not only with text but also images.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.4 s.22
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• pp.31-39
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• 2002

In this paper, we propose Media CIS system architecture which provides geographical information of geo-features in video sequences by using 3D geographical database and GPS-related data resulted from 4S-Van. We introduce a novel interoperable geographical data service concept, so-called, Virtual World Mapping (VWA) that can map 3D graphic world with real-world video to provide geographical information. Our proposed method can easily retrieve geographical information and attributes to reconstruct 3D virtual space according to certain frame in video sequences. Our proposed system architecture also has an advantage that can provide geographical information service with video stream without any image processing procedures. In addition to, describing the details of our components, we present a Media GIS web service system by using GeoVideo Server, which performs VWM technique.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.