• The Journal of Korean Association of Computer Education
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• v.4 no.2
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• pp.85-95
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• 2001

Since documents on the Web are naturally partitioned into many document databases, the efficient information retrieval process requires identifying the document databases that are most likely to provide relevant documents to the query and then querying the identified document databases. We propose a neural net based user feedback learning mechanism for such an efficient information retrieval. Presented learning mechanism learns about underlying document databases using the relevance feedbacks obtained from user's retrieval experiences. For a given query, the learning mechanism, which is sufficiently trained, discovers the document databases associated with the relevant documents and retrieves those documents effectively.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1498-1504
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• 2016

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.51-55
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• 2001

With the rapid growth of the World Wide Web and electronic information services, information is becoming available on-Line at an incredible rate. One result is the oft-decried information overload. No one has time to read everything, yet we often have to make critical decisions based on what we are able to assimilate. The technology of automatic text summarization is becoming indispensable for dealing with this problem. Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task. Information retrieval(IR) is the task of searching a set of documents for some query-relevant documents. On the other hand, text summarization is considered to be the task of searching a document, a set of sentences, for some topic-relevant sentences. In this paper, we show that document information, that is more reliable and suitable for query, using document length normalization of which is gained through information retrieval . Experimental results of this system in newspaper articles show that document length normalization method superior to other methods use query itself.

• KSCI Review
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• v.12 no.1
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• pp.9-19
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• 2004

This paper proposes ACA (Adaptive Component Analysis) as a method for feature extraction and analysis of the content-based image retrieval system. For satisfactory retrieval, the features extracted from images should be appropriately applied according to the image domains and for this. retrieval measurement is Proposed in this study. Retrieval measurement is a standard indicating how important the value of a relevant feature is to image retrieval ACA is a middle stage for content-based image retrieval and it purposes to improve the retrieval speed and performance.

• Journal of the Korean Society for information Management
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• v.7 no.1
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• pp.79-95
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• 1990

A retrieval technique was defined as a technique for comparing the document representations. So this study classified retrieval technique in terms of the charactristics of the retrieved set of documents and the representations that are used. The distinction is whether the set of retrieved documents contains only documents whose representations are an exact match with the query, or a partial match with query. For a partial match, the set of retrieved document will include also those that are an exact match with the query. Boolean-logic as one of the exact match retrieval techniques is in current in most of the large operational information retrieval systems despite of its problems and limitatlons. Partial match as an alternative technique has also various problems. Existing information retrieval systems are successful in aSSisting the user whose needs are well- defined (e.g. Boolean-logic), to retrieve relevant documents but it should be successful in providing retrieval assistance to the browser whose information requirements is ill-defined.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.52-64
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• 2015

Recently, as the quality of life has been improving, search items in the area of entertainment represent an increasing share of the total usage of Internet portal sites. Information retrieval in the entertainment area is mainly depending on keywords that users are inputting, and the results of information retrieval are the contents that contain those keywords. In this paper, we propose a search method that takes natural language inputs and retrieves the database pertaining to entertainment. The main components of our study are the simple Korean morphological analyzer using case particle information, predicate-oriented token generation, standardized pattern generation coherent to tokens, and automatic generation of the corresponding SQL queries. We also propose an efficient retrieval system that searches the most relevant results from the database in terms of natural language querying, especially in the restricted domain of music, and shows the effectiveness of our system.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.247-256
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• 2009

Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.

• Journal of the Korean Society for information Management
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• v.12 no.2
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• pp.225-232
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• 1995

It has been known that test collections play an important role for researches in information retrieval. A variety of test collections have been created in foreign countries, and have been heavily used by researchers. Although research interests in Hangul information retrieval have been rapidly grown up in Korea these days, lack of Hangul test collec tions makes it difficult to develop retrieval techniques for Hangul texts. This study describes the development of the KRIST test collection. The KRIST test collection consists of 13.515 bibliographic records. 30 queries and a list of relevant documents to the queries.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.57-62
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• 2008

Image retrieval is one of the most exciting and rapidly growing research issues in the field of multimedia technology. This paper proposes a new method that performs search the relevant images by using query-by-example. The proposed method for search and retrieval of images utilizes the location information where the image had been taken. The system associates the photo images with their corresponding GPS coordinates that are used as metadata for searching. Experimental results show that the proposed method demonstrates better performance improving up to 59% of average recall and 49% of average precision. Moreover, we learned from the experimental results geo-location information embedded within the image header is more effective and positive on the search and storage.

• Journal of the Semiconductor & Display Technology
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• v.13 no.2
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• pp.45-52
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• 2014

This paper proposes an efficient photo retrieval system that automatically indexes for searching of relevant images, using a combination of geo-coded information, direction/location of image capture device and content-based visual features. A photo image is labeled with its GPS (Global Positioning System) coordinates and direction of the camera view at the moment of capture, and the label leads to generate a geo-spatial index with three core elements of latitude, longitude and viewing direction. Then, content-based visual features are extracted and combined with the geo-spatial information, for indexing and retrieving the photo images. For user's querying process, the proposed method adopts two steps as a progressive approach, filtering the relevant subset prior to use a content-based ranking function. To evaluate the performance of the proposed scheme, we assess the simulation performance in terms of average precision and F-score, using a natural photo collection. Comparing the proposed approach to retrieve using only visual features, an improvement of 20.8% was observed. The experimental results show that the proposed method exhibited a significant enhancement of around 7.2% in retrieval effectiveness, compared to previous work. These results reveal that a combination of context and content analysis is markedly more efficient and meaningful that using only visual feature for image search.