• Title/Summary/Keyword: Indexing Mechanism

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Image Retrieval using Distribution Block Signature of Main Colors' Set and Performance Boosting via Relevance feedback (주요 색상의 분포 블록기호를 이용한 영상검색과 유사도 피드백을 통한 이미지 검색)

  • 박한수;유헌우;장동식
    • Journal of KIISE:Software and Applications
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    • v.31 no.2
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    • pp.126-136
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    • 2004
  • This paper proposes a new content-based image retrieval algorithm using color-spatial information. For the purpose, the paper suggests two kinds of indexing key to prune away irrelevant images to a given query image; MCS(Main Colors' Set), which is related with color information and DBS (Distribution Block Signature), which is related with spatial information. After successively applying these filters to a database, we could get a small amount of high potential candidates that are somewhat similar to the query image. Then we would make use of new QM(Quad modeling) and relevance feedback mechanism to obtain more accurate retrieval. It would enhance the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed algorithm can apply successfully image retrieval applications.

Mapping Publication Pattern in African Journal of Library, Archives and Information Science, 2009-2018: An Informetric Study

  • Amusan, Blessing Babawale;Adeyoyin, Samuel Olu
    • International Journal of Knowledge Content Development & Technology
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    • v.12 no.1
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    • pp.17-34
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    • 2022
  • This informetrics study was conducted to find out the distribution of articles and authors that published in African Journal of Library, Archives and Information Studies [AJLAIS]) from 2009 to 2018; considering the year-wise growth of research articles; authorship pattern and collaboration ratio; subject and geographical distributions of authors; and authors' productivity level. A descriptive informetrics research design was adopted. Quota sampling technique was used to select all the articles published within the ten-year period. Data collected through a self-designed checklist was analyzed using frequency count and percentage. The findings revealed that 141 articles, contributed by 266 authors were published by AJLAIS during the period. An annual average growth of 1.20% was recorded. Overall year-wise authorship pattern revealed that majority of articles (62.41%) published in AJLAIS were multiple authored. Also, articles on Informetrics and ICT dominated the journal. Some subject areas not covered were identified such as: indexing and serial collections management. Average collaborative index across the 10-year period for the journal was 0.62. South Africa and Nigeria were the two major prolific contributors to AJLAIS, just as evidence-based research papers of survey type (65.25%) were the most common to the journal. There should be increased numbers of articles in each edition over the coming years, and awareness should be created by the publishers to familiarize the researchers with the publishing requirements of the journal. Also, LIS researchers should concentrate more on areas usually left untouched by previous studies. The study is original as no other similar study was found on publication pattern of articles in AJLAIS covering a ten year period of 2009-2018. The findings of the study will also serve as a feedback mechanism for the Publisher of the Journal and LIS researchers on how to improve the journal and LIS research in general.

The Recognition of Occluded 2-D Objects Using the String Matching and Hash Retrieval Algorithm (스트링 매칭과 해시 검색을 이용한 겹쳐진 이차원 물체의 인식)

  • Kim, Kwan-Dong;Lee, Ji-Yong;Lee, Byeong-Gon;Ahn, Jae-Hyeong
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.7
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    • pp.1923-1932
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    • 1998
  • This paper deals with a 2-D objects recognition algorithm. And in this paper, we present an algorithm which can reduce the computation time in model retrieval by means of hashing technique instead of using the binary~tree method. In this paper, we treat an object boundary as a string of structural units and use an attributed string matching algorithm to compute similarity measure between two strings. We select from the privileged strings a privileged string wIth mmimal eccentricity. This privileged string is treated as the reference string. And thell we wllstructed hash table using the distance between privileged string and the reference string as a key value. Once the database of all model strings is built, the recognition proceeds by segmenting the scene into a polygonal approximation. The distance between privileged string extracted from the scene and the reference string is used for model hypothesis rerieval from the table. As a result of the computer simulation, the proposed method can recognize objects only computing, the distance 2-3tiems, while previous method should compute the distance 8-10 times for model retrieval.

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