• Journal of the Korean Society for Library and Information Science
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• v.37 no.4
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• pp.178-198
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• 2003

Mutual information as an association measure, has been used for various purposes as well as for calculating term similarity. There we, however, some limits in mutual information. It tends to emphasize low frequency terms extremely because the marginal value of mutual information changes inversely to frequency of terms. To compensate for this limit this study suggests relative mutual information(RMI) coefficients which normalize mutual information, and examines their characteristics in some details. The RMI coefficients also improve effectiveness of global query expansion when they are adapted to three different collections.

• ETRI Journal
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• v.21 no.1
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• pp.40-54
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• 1999

In this paper, we describe a new trademark retrieval system based upon the content or the shape of trademark. The system has an on-line graphical user interface for the World Wide Web (WWW) that allows user to provide a query in forms of a sketch or a visual image to search for similar trademarks from database. User interfaces for the WWW were implemented by utilizing HTML and Java applets. The query can occur in arbitrary size and orientation. A shape representation scheme invariant to scale and rotation was developed to measure the similarity between two trademarks using the magnitude of Zernike moments as a feature set. Performance evaluation has been carried out with a database of 3,000 trademarks. It takes only about 0.6 second for the retrieval on a 200 MHz Pentium PC. The average recall of the original one among top 30 candidates queried by noisy or deformed images was 100%.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1270-1281
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• 1999

피라미드 기법 1 은 d-차원의 공간을 2d개의 피라미드들로 분할하는 특별한 공간 분할 방식을 이용하여 고차원 데이타를 효율적으로 색인할 수 있는 새로운 색인 방법으로 제안되었다. 피라미드 기법은 고차원 사각형 형태의 영역 질의에는 효율적이나, 유사성 검색에 많이 사용되는 고차원 구형태의 영역 질의에는 비효율적인 면이 존재한다. 본 논문에서는 고차원 데이타를 많이 사용하는 유사성 검색에 효율적인 새로운 색인 기법으로 구형 피라미드 기법을 제안한다. 구형 피라미드 기법은 먼저 d-차원의 공간을 2d개의 구형 피라미드로 분할하고, 각 단일 구형 피라미드를 다시 구형태의 조각으로 분할하는 특별한 공간 분할 방법에 기반하고 있다. 이러한 공간 분할 방식은 피라미드 기법과 마찬가지로 d-차원 공간을 1-차원 공간으로 변환할 수 있다. 따라서, 변환된 1-차원 데이타를 다루기 위하여 B+-트리를 사용할 수 있다. 본 논문에서는 이렇게 분할된 공간에서 고차원 구형태의 영역 질의를 효율적으로 처리할 수 있는 알고리즘을 제안한다. 마지막으로, 인위적 데이타와 실제 데이타를 사용한 다양한 실험을 통하여 구형 피라미드 기법이 구형태의 영역 질의를 처리하는데 있어서 기존의 피라미드 기법보다 효율적임을 보인다.Abstract The Pyramid-Technique 1 was proposed as a new indexing method for high- dimensional data spaces using a special partitioning strategy that divides d-dimensional space into 2d pyramids. It is efficient for hypercube range query, but is not efficient for hypersphere range query which is frequently used in similarity search. In this paper, we propose the Spherical Pyramid-Technique, an efficient indexing method for similarity search in high-dimensional space. The Spherical Pyramid-Technique is based on a special partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space as the Pyramid-Technique does. Thus, we are able to use a B+-tree to manage the transformed 1-dimensional data. We also propose the algorithm of processing hypersphere range query on the space partitioned by this partitioning strategy. Finally, we show that the Spherical Pyramid-Technique clearly outperforms the Pyramid-Technique in processing hypersphere range queries through various experiments using synthetic and real data.

• Journal of KIISE:Software and Applications
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• v.31 no.12
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• pp.1660-1671
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• 2004

This paper proposes a method for measuring document similarity between two documents. One of the most significant ideas of the method is to estimate the degree of similarity between two documents based on the frequencies of terms and term-pair, existing in both the two documents. In contrast to conventional methods which takes only one feature into account, the proposed method considers several features at the same time and meatures the similarity using a neural network. To prove the superiority of our method, two experiments have been conducted. One is to verify whether the two input documents are from the same document or not. The other is a problem of information retrieval with a document as the query against a large number of documents. In both the two experiments, the proposed method shows higher accuracy than two conventional methods, Cosine similarity measurement and a term-pair method.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.151-155
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• 2009

We proposes a new method using the non-negative matrix factorization (NMF) and clustering method to extract the sentences for personalized document summarization. The proposed method uses clustering method for retrieving documents to extract sentences which are well reflected topics and sub-topics in document. Beside it can extract sentences with respect to query which are well reflected user interesting by using the inherent semantic features in document by NMF. The experimental results shows that the proposed method achieves better performance than other methods use the similarity and the NMF.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.21-23
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• 1999

기존 이미지 데이터베이스 검색 시스템은 질의 결과로 유사도가 높은 이미지들을 모아 제시해 준다. 그런데, 질의 결과로 제시되는 이미지는 전부 같은 크기의 이미지로 표시되고 일차원적으로 단순 나열하기 때문에 결과 이미지의 의미 파악이나 질의 처리 과정의 정보를 확인하는 것이 명확하지 않다. 또한 하나의 화면에 나타낼 수 있는 이미지의 수도 매우 제한된다. 따라서 본 논문은 이미지가 표현되는 화면 공간을 5개의 Wave로 나누고, 유사도 순서로 영역 크기를 할당하여 질의 결과 이미지들을 이차원으로 배열하여 제시하고 관리하는 방안을 제안하는 것이다. 그래서 한정된 화면 공간의 활용도를 극대화하고, 질의 결과에 대한 사용자 인지도를 높이며, 질의 결과의 유사도 정보를 유추하거나 분석하기가 용이한 방안이 될 것이다.

• Genomics & Informatics
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• v.20 no.3
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• pp.36.1-36.6
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• 2022

BLAST, a basic bioinformatics tool for searching local sequence similarity, has been one of the most widely used bioinformatics programs since its introduction in 1990. Users generally use the web-based NCBI-BLAST program for BLAST analysis. However, users with large sequence data are often faced with a problem of upload size limitation while using the web-based BLAST program. This proves inconvenient as scientists often want to run BLAST on their own data, such as transcriptome or whole genome sequences. To overcome this issue, we developed NBLAST, a graphical user interface-based BLAST program that employs a two-way system, allowing the use of input sequences either as "query" or "target" in the BLAST analysis. NBLAST is also equipped with a dot plot viewer, thus allowing researchers to create custom database for BLAST and run a dot plot similarity analysis within a single program. It is available to access to the NBLAST with http://nbitglobal.com/nblast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.117-128
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• 2001

In order to achieve optimal retrieval, i.e., to find the best match to a query according to a certain similarity measure, the exhaustive search should be performed literally for all the images in a database. However, the straightforward exhaustive search algorithm is computationally expensive in large image databases. To reduce its heavy computational cost, this paper presents a fast exhaustive multi-resolution search algorithm based on image database clustering. Firstly, the proposed algorithm partitions the whole image data set into a pre-defined number of clusters having similar feature contents. Next, for a given query, it checks the lower bound of distances in each cluster, eliminating disqualified clusters. Then, it only examines the candidates in the remaining clusters. To alleviate unnecessary feature matching operations in the search procedure, the distance inequality property is employed based on a multi-resolution data structure. The proposed algorithm realizes a fast exhaustive multi-resolution search for either the best match or multiple best matches to the query. Using luminance histograms as a feature, we prove that the proposed algorithm guarantees optimal retrieval with high searching speed.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3874-3884
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• 2000

An information retrieval system has to retrieve all and only documents which are relevant to a user query, even if index terms and query terms are not matched exactly. However, term mismatches between index terms and qucry terms have been a serious obstacle to the enhancement of retrieval performance. In this paper, we discuss automatic term normalization between words in text corpora and their application to a Korean information retrieval system. We perform two types of term normalizations to alleviate semantic term mismatches: equivalence class and co-occurrence cluster. First, transliterations, spelling errors, and synonyms are normalized into equivalence classes bv using contextual similarity. Second, context-based terms are normalized by using a combination of mutual information and word context to establish word similarities. Next, unsupervised clustering is done by using K-means algorithm and co-occurrence clusters are identified. In this paper, these normalized term products are used in the query expansion to alleviate semantic tem1 mismatches. In other words, we utilize two kinds of tcrm normalizations, equivalence class and co-occurrence cluster, to expand user's queries with new tcrms, in an attempt to make user's queries more comprehensive (adding transliterations) or more specific (adding spc'Cializationsl. For query expansion, we employ two complementary methods: term suggestion and term relevance feedback. The experimental results show that our proposed system can alleviatl' semantic term mismatches and can also provide the appropriate similarity measurements. As a result, we know that our system can improve the rctrieval efficiency of the information retrieval system.